{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yfinance as yf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "# pip install urllib3==1.25.8\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.regression.rolling import RollingOLS\n",
    "pd.set_option('display.max_rows', 5)\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "pd.set_option('display.expand_frame_repr', True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  9 of 9 completed\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"9\" halign=\"left\">Adj Close</th>\n",
       "      <th>Close</th>\n",
       "      <th>...</th>\n",
       "      <th>Open</th>\n",
       "      <th colspan=\"9\" halign=\"left\">Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>BRK-B</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>GOOGL</th>\n",
       "      <th>JNJ</th>\n",
       "      <th>JPM</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>SPY</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>...</th>\n",
       "      <th>SPY</th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>BRK-B</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>GOOGL</th>\n",
       "      <th>JNJ</th>\n",
       "      <th>JPM</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>SPY</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-08-19</th>\n",
       "      <td>0.471565</td>\n",
       "      <td>38.630001</td>\n",
       "      <td>57.619999</td>\n",
       "      <td>49.982655</td>\n",
       "      <td>50.220219</td>\n",
       "      <td>35.539684</td>\n",
       "      <td>24.194950</td>\n",
       "      <td>17.377619</td>\n",
       "      <td>78.531418</td>\n",
       "      <td>0.548393</td>\n",
       "      <td>...</td>\n",
       "      <td>109.809998</td>\n",
       "      <td>388920000</td>\n",
       "      <td>12696100</td>\n",
       "      <td>250000</td>\n",
       "      <td>44871361.0</td>\n",
       "      <td>44659096.0</td>\n",
       "      <td>4444400</td>\n",
       "      <td>7859600</td>\n",
       "      <td>46293000</td>\n",
       "      <td>39881600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-20</th>\n",
       "      <td>0.472946</td>\n",
       "      <td>39.509998</td>\n",
       "      <td>57.880001</td>\n",
       "      <td>53.952770</td>\n",
       "      <td>54.209209</td>\n",
       "      <td>35.702423</td>\n",
       "      <td>24.608812</td>\n",
       "      <td>17.428888</td>\n",
       "      <td>79.082588</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>...</td>\n",
       "      <td>109.610001</td>\n",
       "      <td>316780800</td>\n",
       "      <td>6790800</td>\n",
       "      <td>155000</td>\n",
       "      <td>22942874.0</td>\n",
       "      <td>22834343.0</td>\n",
       "      <td>3809300</td>\n",
       "      <td>9293600</td>\n",
       "      <td>46494800</td>\n",
       "      <td>44870900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-23</th>\n",
       "      <td>0.477246</td>\n",
       "      <td>39.450001</td>\n",
       "      <td>57.320000</td>\n",
       "      <td>54.495735</td>\n",
       "      <td>54.754753</td>\n",
       "      <td>35.708675</td>\n",
       "      <td>24.545139</td>\n",
       "      <td>17.506001</td>\n",
       "      <td>78.882133</td>\n",
       "      <td>0.555000</td>\n",
       "      <td>...</td>\n",
       "      <td>110.550003</td>\n",
       "      <td>254660000</td>\n",
       "      <td>5532600</td>\n",
       "      <td>600000</td>\n",
       "      <td>18342897.0</td>\n",
       "      <td>18256126.0</td>\n",
       "      <td>4667300</td>\n",
       "      <td>8000900</td>\n",
       "      <td>39572200</td>\n",
       "      <td>33745100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-24</th>\n",
       "      <td>0.490606</td>\n",
       "      <td>39.049999</td>\n",
       "      <td>56.599998</td>\n",
       "      <td>52.239197</td>\n",
       "      <td>52.487488</td>\n",
       "      <td>35.783798</td>\n",
       "      <td>24.570606</td>\n",
       "      <td>17.506001</td>\n",
       "      <td>78.989525</td>\n",
       "      <td>0.570536</td>\n",
       "      <td>...</td>\n",
       "      <td>110.639999</td>\n",
       "      <td>374136000</td>\n",
       "      <td>7640400</td>\n",
       "      <td>630000</td>\n",
       "      <td>15319808.0</td>\n",
       "      <td>15247337.0</td>\n",
       "      <td>4028100</td>\n",
       "      <td>6038500</td>\n",
       "      <td>40835300</td>\n",
       "      <td>30453100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004-08-25</th>\n",
       "      <td>0.507497</td>\n",
       "      <td>40.299999</td>\n",
       "      <td>57.439999</td>\n",
       "      <td>52.802086</td>\n",
       "      <td>53.053055</td>\n",
       "      <td>36.090492</td>\n",
       "      <td>25.111797</td>\n",
       "      <td>17.705225</td>\n",
       "      <td>79.526390</td>\n",
       "      <td>0.590179</td>\n",
       "      <td>...</td>\n",
       "      <td>110.330002</td>\n",
       "      <td>505618400</td>\n",
       "      <td>7254800</td>\n",
       "      <td>520000</td>\n",
       "      <td>9232276.0</td>\n",
       "      <td>9188602.0</td>\n",
       "      <td>4701100</td>\n",
       "      <td>11953600</td>\n",
       "      <td>53512700</td>\n",
       "      <td>38551400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Adj Close                                                         \\\n",
       "                AAPL       AMZN      BRK-B       GOOG      GOOGL        JNJ   \n",
       "Date                                                                          \n",
       "2004-08-19  0.471565  38.630001  57.619999  49.982655  50.220219  35.539684   \n",
       "2004-08-20  0.472946  39.509998  57.880001  53.952770  54.209209  35.702423   \n",
       "2004-08-23  0.477246  39.450001  57.320000  54.495735  54.754753  35.708675   \n",
       "2004-08-24  0.490606  39.049999  56.599998  52.239197  52.487488  35.783798   \n",
       "2004-08-25  0.507497  40.299999  57.439999  52.802086  53.053055  36.090492   \n",
       "\n",
       "                                                Close  ...        Open  \\\n",
       "                  JPM       MSFT        SPY      AAPL  ...         SPY   \n",
       "Date                                                   ...               \n",
       "2004-08-19  24.194950  17.377619  78.531418  0.548393  ...  109.809998   \n",
       "2004-08-20  24.608812  17.428888  79.082588  0.550000  ...  109.610001   \n",
       "2004-08-23  24.545139  17.506001  78.882133  0.555000  ...  110.550003   \n",
       "2004-08-24  24.570606  17.506001  78.989525  0.570536  ...  110.639999   \n",
       "2004-08-25  25.111797  17.705225  79.526390  0.590179  ...  110.330002   \n",
       "\n",
       "               Volume                                                     \\\n",
       "                 AAPL      AMZN   BRK-B        GOOG       GOOGL      JNJ   \n",
       "Date                                                                       \n",
       "2004-08-19  388920000  12696100  250000  44871361.0  44659096.0  4444400   \n",
       "2004-08-20  316780800   6790800  155000  22942874.0  22834343.0  3809300   \n",
       "2004-08-23  254660000   5532600  600000  18342897.0  18256126.0  4667300   \n",
       "2004-08-24  374136000   7640400  630000  15319808.0  15247337.0  4028100   \n",
       "2004-08-25  505618400   7254800  520000   9232276.0   9188602.0  4701100   \n",
       "\n",
       "                                          \n",
       "                 JPM      MSFT       SPY  \n",
       "Date                                      \n",
       "2004-08-19   7859600  46293000  39881600  \n",
       "2004-08-20   9293600  46494800  44870900  \n",
       "2004-08-23   8000900  39572200  33745100  \n",
       "2004-08-24   6038500  40835300  30453100  \n",
       "2004-08-25  11953600  53512700  38551400  \n",
       "\n",
       "[5 rows x 54 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# download data\n",
    "\n",
    "#########################################\n",
    "# according to 'Top 10 Holdings (27.15% of Total Assets) of SPY'\n",
    "data = yf.download(\"SPY AAPL MSFT AMZN GOOGL GOOG BRK-B JPM JNJ\",start='2000-01-01',end='2021-05-01',interval='1d')\n",
    "#########################################\n",
    "# I got top 100 weighted SS stocks from CSI300 index in below list\n",
    "#data = yf.download(\"3188.HK 601318.SS 600519.SS 600036.SS 601166.SS 600016.SS 600887.SS 601328.SS 601288.SS 600276.SS 600030.SS 600000.SS 601398.SS 601668.SS 600104.SS 601601.SS 600900.SS 601169.SS 600048.SS 600837.SS 601988.SS 600019.SS 601766.SS 600028.SS 600518.SS 600585.SS 601211.SS 601818.SS 600015.SS 600703.SS 601688.SS 600690.SS 600050.SS 601888.SS 601939.SS 600919.SS 600309.SS 601857.SS 601989.SS 601006.SS 600009.SS 601012.SS 601186.SS 601899.SS 600196.SS 601009.SS 601628.SS 601088.SS 601390.SS 600999.SS 600340.SS 600741.SS 601336.SS 600221.SS 600958.SS 601933.SS 600029.SS 600031.SS 600660.SS 600795.SS 603799.SS 600089.SS 601155.SS 601985.SS 601225.SS 601377.SS 601600.SS 601669.SS 600010.SS 600066.SS 600535.SS 600570.SS 600886.SS 600011.SS 600111.SS 600115.SS 600271.SS 600406.SS 600588.SS 601607.SS 600606.SS 600383.SS 600485.SS 600522.SS 601901.SS 600352.SS 600436.SS 600893.SS 600068.SS 600705.SS 601111.SS 601788.SS 600023.SS 600739.SS 601919.SS 603993.SS 600018.SS 600177.SS 600482.SS 600547.SS 600637.SS\",start='2000-01-01',end='2021-05-01',interval='1d')\n",
    "#data.rename(columns={'3188.HK':'SPY'},inplace=True)\n",
    "##########################################\n",
    "\n",
    "data.dropna(how='any',axis=0,inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>BRK-B</th>\n",
       "      <th>GOOG</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-08-20</th>\n",
       "      <td>0.472946</td>\n",
       "      <td>39.509998</td>\n",
       "      <td>57.880001</td>\n",
       "      <td>53.952770</td>\n",
       "      <td>54.209209</td>\n",
       "      <td>35.702423</td>\n",
       "      <td>24.608812</td>\n",
       "      <td>17.428888</td>\n",
       "      <td>79.082588</td>\n",
       "      <td>0.002925</td>\n",
       "      <td>0.022525</td>\n",
       "      <td>0.004502</td>\n",
       "      <td>0.076433</td>\n",
       "      <td>0.076433</td>\n",
       "      <td>0.004569</td>\n",
       "      <td>0.016961</td>\n",
       "      <td>0.002946</td>\n",
       "      <td>0.006994</td>\n",
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       "    <tr>\n",
       "      <th>2004-08-23</th>\n",
       "      <td>0.477246</td>\n",
       "      <td>39.450001</td>\n",
       "      <td>57.320000</td>\n",
       "      <td>54.495735</td>\n",
       "      <td>54.754753</td>\n",
       "      <td>35.708675</td>\n",
       "      <td>24.545139</td>\n",
       "      <td>17.506001</td>\n",
       "      <td>78.882133</td>\n",
       "      <td>0.009050</td>\n",
       "      <td>-0.001520</td>\n",
       "      <td>-0.009722</td>\n",
       "      <td>0.010013</td>\n",
       "      <td>0.010013</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>-0.002591</td>\n",
       "      <td>0.004415</td>\n",
       "      <td>-0.002538</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2021-04-29</th>\n",
       "      <td>133.253662</td>\n",
       "      <td>3471.310059</td>\n",
       "      <td>277.600006</td>\n",
       "      <td>2429.889893</td>\n",
       "      <td>2392.760010</td>\n",
       "      <td>164.199997</td>\n",
       "      <td>155.190002</td>\n",
       "      <td>252.509995</td>\n",
       "      <td>420.059998</td>\n",
       "      <td>-0.000749</td>\n",
       "      <td>0.003697</td>\n",
       "      <td>0.016893</td>\n",
       "      <td>0.020783</td>\n",
       "      <td>0.014193</td>\n",
       "      <td>0.013612</td>\n",
       "      <td>0.019258</td>\n",
       "      <td>-0.008086</td>\n",
       "      <td>0.006353</td>\n",
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       "    <tr>\n",
       "      <th>2021-04-30</th>\n",
       "      <td>131.237091</td>\n",
       "      <td>3467.419922</td>\n",
       "      <td>274.950012</td>\n",
       "      <td>2410.120117</td>\n",
       "      <td>2353.500000</td>\n",
       "      <td>162.729996</td>\n",
       "      <td>153.809998</td>\n",
       "      <td>252.179993</td>\n",
       "      <td>417.299988</td>\n",
       "      <td>-0.015249</td>\n",
       "      <td>-0.001121</td>\n",
       "      <td>-0.009592</td>\n",
       "      <td>-0.008169</td>\n",
       "      <td>-0.016544</td>\n",
       "      <td>-0.008993</td>\n",
       "      <td>-0.008932</td>\n",
       "      <td>-0.001308</td>\n",
       "      <td>-0.006592</td>\n",
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       "</table>\n",
       "<p>4203 rows × 18 columns</p>\n",
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      ],
      "text/plain": [
       "                  AAPL         AMZN       BRK-B         GOOG        GOOGL  \\\n",
       "Date                                                                        \n",
       "2004-08-20    0.472946    39.509998   57.880001    53.952770    54.209209   \n",
       "2004-08-23    0.477246    39.450001   57.320000    54.495735    54.754753   \n",
       "...                ...          ...         ...          ...          ...   \n",
       "2021-04-29  133.253662  3471.310059  277.600006  2429.889893  2392.760010   \n",
       "2021-04-30  131.237091  3467.419922  274.950012  2410.120117  2353.500000   \n",
       "\n",
       "                   JNJ         JPM        MSFT         SPY  return_AAPL  \\\n",
       "Date                                                                      \n",
       "2004-08-20   35.702423   24.608812   17.428888   79.082588     0.002925   \n",
       "2004-08-23   35.708675   24.545139   17.506001   78.882133     0.009050   \n",
       "...                ...         ...         ...         ...          ...   \n",
       "2021-04-29  164.199997  155.190002  252.509995  420.059998    -0.000749   \n",
       "2021-04-30  162.729996  153.809998  252.179993  417.299988    -0.015249   \n",
       "\n",
       "            return_AMZN  return_BRK-B  return_GOOG  return_GOOGL  return_JNJ  \\\n",
       "Date                                                                           \n",
       "2004-08-20     0.022525      0.004502     0.076433      0.076433    0.004569   \n",
       "2004-08-23    -0.001520     -0.009722     0.010013      0.010013    0.000175   \n",
       "...                 ...           ...          ...           ...         ...   \n",
       "2021-04-29     0.003697      0.016893     0.020783      0.014193    0.013612   \n",
       "2021-04-30    -0.001121     -0.009592    -0.008169     -0.016544   -0.008993   \n",
       "\n",
       "            return_JPM  return_MSFT  return_SPY  \n",
       "Date                                             \n",
       "2004-08-20    0.016961     0.002946    0.006994  \n",
       "2004-08-23   -0.002591     0.004415   -0.002538  \n",
       "...                ...          ...         ...  \n",
       "2021-04-29    0.019258    -0.008086    0.006353  \n",
       "2021-04-30   -0.008932    -0.001308   -0.006592  \n",
       "\n",
       "[4203 rows x 18 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# compute return\n",
    "\n",
    "df = data['Adj Close']\n",
    "\n",
    "for i in df.columns:\n",
    "    if 'return' not in df:\n",
    "        df['return_'+i] = np.log(df[i]).diff()#.shift(-1)\n",
    "        \n",
    "df = df[1:]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CSSD - Christle and Huang (1995)\n",
    "CSSD ( cross section standard deviation of variance) gives how far individual stock deviate from index in % everyday\n",
    "if extreme market volatility coincides with low CSSD, then means market is herding or irrational and forgets stocks as individual\n",
    "as in behavior finance theory, index pnl is driven by large sector constituents. So market should have both winners and losers\n",
    "\n",
    "$$CSSD=a+\\beta_1*bull+\\beta_2*bear+\\epsilon$$\n",
    "\n",
    "this test looks at the t-statistic for both $\\beta_1$ and $\\beta_2$\n",
    "\n",
    "### CSAD - Chang at al.(2000) CSAD regression\n",
    "cross sectional absolute deviation\n",
    "\n",
    "$$CSAD=a+\\beta_1*R_m+\\beta_2*|R_m|+\\beta_3*R_m^2+\\epsilon$$\n",
    "\n",
    "this test looks at the t-statistic for only $\\beta_3$\n",
    "\n",
    "https://www.youtube.com/watch?v=_s1LJ_63hyg&list=PLE4a3phdCOavPRUkIQeoiuRwGzZfloqtE&index=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>BRK-B</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>GOOGL</th>\n",
       "      <th>JNJ</th>\n",
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       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>2004-08-20</th>\n",
       "      <td>0.472946</td>\n",
       "      <td>39.509998</td>\n",
       "      <td>57.880001</td>\n",
       "      <td>53.952770</td>\n",
       "      <td>54.209209</td>\n",
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       "      <th>2004-08-23</th>\n",
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       "      <td>0.010013</td>\n",
       "      <td>0.010013</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>-0.002591</td>\n",
       "      <td>0.004415</td>\n",
       "      <td>-0.002538</td>\n",
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       "      <th>2021-04-29</th>\n",
       "      <td>133.253662</td>\n",
       "      <td>3471.310059</td>\n",
       "      <td>277.600006</td>\n",
       "      <td>2429.889893</td>\n",
       "      <td>2392.760010</td>\n",
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       "      <th>2021-04-30</th>\n",
       "      <td>131.237091</td>\n",
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       "                  AAPL         AMZN       BRK-B         GOOG        GOOGL  \\\n",
       "Date                                                                        \n",
       "2004-08-20    0.472946    39.509998   57.880001    53.952770    54.209209   \n",
       "2004-08-23    0.477246    39.450001   57.320000    54.495735    54.754753   \n",
       "...                ...          ...         ...          ...          ...   \n",
       "2021-04-29  133.253662  3471.310059  277.600006  2429.889893  2392.760010   \n",
       "2021-04-30  131.237091  3467.419922  274.950012  2410.120117  2353.500000   \n",
       "\n",
       "                   JNJ         JPM        MSFT         SPY  return_AAPL  \\\n",
       "Date                                                                      \n",
       "2004-08-20   35.702423   24.608812   17.428888   79.082588     0.002925   \n",
       "2004-08-23   35.708675   24.545139   17.506001   78.882133     0.009050   \n",
       "...                ...         ...         ...         ...          ...   \n",
       "2021-04-29  164.199997  155.190002  252.509995  420.059998    -0.000749   \n",
       "2021-04-30  162.729996  153.809998  252.179993  417.299988    -0.015249   \n",
       "\n",
       "            return_AMZN  return_BRK-B  return_GOOG  return_GOOGL  return_JNJ  \\\n",
       "Date                                                                           \n",
       "2004-08-20     0.022525      0.004502     0.076433      0.076433    0.004569   \n",
       "2004-08-23    -0.001520     -0.009722     0.010013      0.010013    0.000175   \n",
       "...                 ...           ...          ...           ...         ...   \n",
       "2021-04-29     0.003697      0.016893     0.020783      0.014193    0.013612   \n",
       "2021-04-30    -0.001121     -0.009592    -0.008169     -0.016544   -0.008993   \n",
       "\n",
       "            return_JPM  return_MSFT  return_SPY      CSSD      CSAD  \n",
       "Date                                                                 \n",
       "2004-08-20    0.016961     0.002946    0.006994  0.037851  0.022176  \n",
       "2004-08-23   -0.002591     0.004415   -0.002538  0.008926  0.006826  \n",
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       "2021-04-30   -0.008932    -0.001308   -0.006592  0.006030  0.004835  \n",
       "\n",
       "[4203 rows x 20 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate CSSD and CSAD\n",
    "\n",
    "df['CSSD'] = 0\n",
    "df['CSAD'] = 0 \n",
    "\n",
    "for i in df.loc[:,~df.columns.str.contains('SPY')&df.columns.str.contains('return')]:\n",
    "    number_stocks = len(df.loc[:,~df.columns.str.contains('SPY')&df.columns.str.contains('return')].columns)\n",
    "    df['CSSD'] += np.power(df[i]-df['return_SPY'],2)/(number_stocks-1)\n",
    "    df['CSAD'] += np.abs(df[i]-df['return_SPY'])/number_stocks\n",
    "\n",
    "df['CSSD'] = np.sqrt(df['CSSD'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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      "-0.00560590726884058\n",
      "0.007005769504029227\n"
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    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>BRK-B</th>\n",
       "      <th>GOOG</th>\n",
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       "      <th>return_SPY</th>\n",
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       "      <th>bear</th>\n",
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       "      <th>Date</th>\n",
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       "      <th>2004-08-20</th>\n",
       "      <td>0.472946</td>\n",
       "      <td>39.509998</td>\n",
       "      <td>57.880001</td>\n",
       "      <td>53.952770</td>\n",
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       "      <td>35.702423</td>\n",
       "      <td>24.608812</td>\n",
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       "      <td>79.082588</td>\n",
       "      <td>0.002925</td>\n",
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       "      <th>2004-08-23</th>\n",
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       "      <td>0.004415</td>\n",
       "      <td>-0.002538</td>\n",
       "      <td>0.008926</td>\n",
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       "      <th>2021-04-29</th>\n",
       "      <td>133.253662</td>\n",
       "      <td>3471.310059</td>\n",
       "      <td>277.600006</td>\n",
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       "      <td>420.059998</td>\n",
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       "      <td>0.014193</td>\n",
       "      <td>0.013612</td>\n",
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       "      <td>0.006353</td>\n",
       "      <td>0.011123</td>\n",
       "      <td>0.009646</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-04-30</th>\n",
       "      <td>131.237091</td>\n",
       "      <td>3467.419922</td>\n",
       "      <td>274.950012</td>\n",
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       "      <td>-0.008932</td>\n",
       "      <td>-0.001308</td>\n",
       "      <td>-0.006592</td>\n",
       "      <td>0.006030</td>\n",
       "      <td>0.004835</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4203 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  AAPL         AMZN       BRK-B         GOOG        GOOGL  \\\n",
       "Date                                                                        \n",
       "2004-08-20    0.472946    39.509998   57.880001    53.952770    54.209209   \n",
       "2004-08-23    0.477246    39.450001   57.320000    54.495735    54.754753   \n",
       "...                ...          ...         ...          ...          ...   \n",
       "2021-04-29  133.253662  3471.310059  277.600006  2429.889893  2392.760010   \n",
       "2021-04-30  131.237091  3467.419922  274.950012  2410.120117  2353.500000   \n",
       "\n",
       "                   JNJ         JPM        MSFT         SPY  return_AAPL  ...  \\\n",
       "Date                                                                     ...   \n",
       "2004-08-20   35.702423   24.608812   17.428888   79.082588     0.002925  ...   \n",
       "2004-08-23   35.708675   24.545139   17.506001   78.882133     0.009050  ...   \n",
       "...                ...         ...         ...         ...          ...  ...   \n",
       "2021-04-29  164.199997  155.190002  252.509995  420.059998    -0.000749  ...   \n",
       "2021-04-30  162.729996  153.809998  252.179993  417.299988    -0.015249  ...   \n",
       "\n",
       "            return_GOOG  return_GOOGL  return_JNJ  return_JPM  return_MSFT  \\\n",
       "Date                                                                         \n",
       "2004-08-20     0.076433      0.076433    0.004569    0.016961     0.002946   \n",
       "2004-08-23     0.010013      0.010013    0.000175   -0.002591     0.004415   \n",
       "...                 ...           ...         ...         ...          ...   \n",
       "2021-04-29     0.020783      0.014193    0.013612    0.019258    -0.008086   \n",
       "2021-04-30    -0.008169     -0.016544   -0.008993   -0.008932    -0.001308   \n",
       "\n",
       "            return_SPY      CSSD      CSAD  bear  bull  \n",
       "Date                                                    \n",
       "2004-08-20    0.006994  0.037851  0.022176     0     0  \n",
       "2004-08-23   -0.002538  0.008926  0.006826     0     0  \n",
       "...                ...       ...       ...   ...   ...  \n",
       "2021-04-29    0.006353  0.011123  0.009646     0     0  \n",
       "2021-04-30   -0.006592  0.006030  0.004835     1     0  \n",
       "\n",
       "[4203 rows x 22 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get bull and bear situations when market volatility is high in both \n",
    "\n",
    "df['bear'] = 0\n",
    "df['bull'] = 0\n",
    "lower_index = np.percentile(df['return_SPY'],20)\n",
    "print(lower_index)\n",
    "upper_index = np.percentile(df['return_SPY'],80)\n",
    "print(upper_index)\n",
    "\n",
    "for i in range(0,len(df)):\n",
    "    df['bear'][i] = 1 if df['return_SPY'][i] < lower_index else 0\n",
    "    df['bull'][i] = 1 if df['return_SPY'][i] > upper_index else 0\n",
    "\n",
    "#pd.set_option('display.max_rows', 100)\n",
    "display(df)\n",
    "pd.set_option('display.max_rows', 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.007868248516978916\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AAPL</th>\n",
       "      <th>AMZN</th>\n",
       "      <th>BRK-B</th>\n",
       "      <th>GOOG</th>\n",
       "      <th>GOOGL</th>\n",
       "      <th>JNJ</th>\n",
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       "      <th>return_AAPL</th>\n",
       "      <th>...</th>\n",
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       "      <th>return_JPM</th>\n",
       "      <th>return_MSFT</th>\n",
       "      <th>return_SPY</th>\n",
       "      <th>CSSD</th>\n",
       "      <th>CSAD</th>\n",
       "      <th>bear</th>\n",
       "      <th>bull</th>\n",
       "      <th>signal</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2004-08-20</th>\n",
       "      <td>0.472946</td>\n",
       "      <td>39.509998</td>\n",
       "      <td>57.880001</td>\n",
       "      <td>53.952770</td>\n",
       "      <td>54.209209</td>\n",
       "      <td>35.702423</td>\n",
       "      <td>24.608812</td>\n",
       "      <td>17.428888</td>\n",
       "      <td>79.082588</td>\n",
       "      <td>0.002925</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004569</td>\n",
       "      <td>0.016961</td>\n",
       "      <td>0.002946</td>\n",
       "      <td>0.006994</td>\n",
       "      <td>0.037851</td>\n",
       "      <td>0.022176</td>\n",
       "      <td>0</td>\n",
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       "      <td>-0.0</td>\n",
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       "    <tr>\n",
       "      <th>2004-08-23</th>\n",
       "      <td>0.477246</td>\n",
       "      <td>39.450001</td>\n",
       "      <td>57.320000</td>\n",
       "      <td>54.495735</td>\n",
       "      <td>54.754753</td>\n",
       "      <td>35.708675</td>\n",
       "      <td>24.545139</td>\n",
       "      <td>17.506001</td>\n",
       "      <td>78.882133</td>\n",
       "      <td>0.009050</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>-0.002591</td>\n",
       "      <td>0.004415</td>\n",
       "      <td>-0.002538</td>\n",
       "      <td>0.008926</td>\n",
       "      <td>0.006826</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2021-04-28</th>\n",
       "      <td>133.353485</td>\n",
       "      <td>3458.500000</td>\n",
       "      <td>272.950012</td>\n",
       "      <td>2379.909912</td>\n",
       "      <td>2359.040039</td>\n",
       "      <td>161.979996</td>\n",
       "      <td>152.229996</td>\n",
       "      <td>254.559998</td>\n",
       "      <td>417.399994</td>\n",
       "      <td>-0.006045</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.007381</td>\n",
       "      <td>0.006458</td>\n",
       "      <td>-0.028693</td>\n",
       "      <td>-0.000287</td>\n",
       "      <td>0.020520</td>\n",
       "      <td>0.015428</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2021-04-29</th>\n",
       "      <td>133.253662</td>\n",
       "      <td>3471.310059</td>\n",
       "      <td>277.600006</td>\n",
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       "      <td>-0.008086</td>\n",
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       "      <td>0.011123</td>\n",
       "      <td>0.009646</td>\n",
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       "<p>4202 rows × 24 columns</p>\n",
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      ],
      "text/plain": [
       "                  AAPL         AMZN       BRK-B         GOOG        GOOGL  \\\n",
       "Date                                                                        \n",
       "2004-08-20    0.472946    39.509998   57.880001    53.952770    54.209209   \n",
       "2004-08-23    0.477246    39.450001   57.320000    54.495735    54.754753   \n",
       "...                ...          ...         ...          ...          ...   \n",
       "2021-04-28  133.353485  3458.500000  272.950012  2379.909912  2359.040039   \n",
       "2021-04-29  133.253662  3471.310059  277.600006  2429.889893  2392.760010   \n",
       "\n",
       "                   JNJ         JPM        MSFT         SPY  return_AAPL  ...  \\\n",
       "Date                                                                     ...   \n",
       "2004-08-20   35.702423   24.608812   17.428888   79.082588     0.002925  ...   \n",
       "2004-08-23   35.708675   24.545139   17.506001   78.882133     0.009050  ...   \n",
       "...                ...         ...         ...         ...          ...  ...   \n",
       "2021-04-28  161.979996  152.229996  254.559998  417.399994    -0.006045  ...   \n",
       "2021-04-29  164.199997  155.190002  252.509995  420.059998    -0.000749  ...   \n",
       "\n",
       "            return_JNJ  return_JPM  return_MSFT  return_SPY      CSSD  \\\n",
       "Date                                                                    \n",
       "2004-08-20    0.004569    0.016961     0.002946    0.006994  0.037851   \n",
       "2004-08-23    0.000175   -0.002591     0.004415   -0.002538  0.008926   \n",
       "...                ...         ...          ...         ...       ...   \n",
       "2021-04-28   -0.007381    0.006458    -0.028693   -0.000287  0.020520   \n",
       "2021-04-29    0.013612    0.019258    -0.008086    0.006353  0.011123   \n",
       "\n",
       "                CSAD  bear  bull  signal  profit  \n",
       "Date                                              \n",
       "2004-08-20  0.022176     0     0       0    -0.0  \n",
       "2004-08-23  0.006826     0     0       0     0.0  \n",
       "...              ...   ...   ...     ...     ...  \n",
       "2021-04-28  0.015428     0     0       0     0.0  \n",
       "2021-04-29  0.009646     0     0       0    -0.0  \n",
       "\n",
       "[4202 rows x 24 columns]"
      ]
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     "metadata": {},
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    {
     "name": "stdout",
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     "text": [
      "Annualized return: 1.25%\n",
      "Annualized Sharpe ratio: 0.82\n",
      "Single max drawdown: -3.0302%\n",
      "Average number of trades generated annually: 20\n"
     ]
    },
    {
     "data": {
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1nNaJglMREREREZHaKC+D6Y9DSkezANKuFfDumbBrlVMnvbv5AvWc1lGD9zkVERERERHZJzwx0KTeK/MCfH2bk7csiEk0efWc1ol6TkVEREREpGVwlcOij00abFmrIXeryV8xzQzt9Vg9xbeu55x6TutEwamIiIiIiDR/+VlwXyuYdDHMeS349//gApO2HQAdh5mhvZWd+5FJI6NNqp7TOlFwKiIiIiIizd+2P518cU7w71+016S9J5r06Ef96/Q+3KSF2SZVz2mdKDgVEREREZHmzeWCN09yjm1XcO9v21CUA32OhkPvNGV9j4K7s506h9zu5LNWmjQ+PbjtaOEUnIqIiIiISPO0cBI8MRjmvemURcY6vZzBkrUaivdCnyMh0muuqWU5+bhU/+va9AtuO1o4BaciIiIiItI8zX4VstfD5zeY4wNuMEGiZ1htMMx6Dt4/1+S7H+x/3oo0aUSAjVDaDwpeO/YBCk5FRERERKT5sV2wfrpvWfdDID4tuD2n39wOO5eZfKse/udHXGhSyyu0ujsb/rETEloFrx37AAWnIiIiIiLS7PRb9pR/YWyy6TkNVnC6d5PvsfcwXo+UTiZN7+ZbLyomOG3Yhyg4FRERERGR5mXNNNpvn2rynUY45bFJ9Q9Ol0yG7+/2LZt8bc3Xjb3ObCHjWcVX6k3BqYiIiIiINB87V8AbJzrH7QY4+bi0+genH1wAM54wK/N6rJnm5IeeF/i66DhnCxlpEAWnIiIiIiLSPNg2TLrYOR5+AUy83zlO6QixKQ0b1luUbVJXOWBBahf4yzI46Zn631NqJcCSUiIiIiIiImFoyWTYvgiATZ2OI/P4p8z8zlNehMho91zPOCgvrdt9y8ucfN5Osz/pfe7FjFylkNIhSN+AVEfBqYiIiIiINA8LPzRbt/zfNlZN/5VMzwJFg89w6kRGQ3lJ3e5bXuzkXaVQWugc526tf3ulTjSsV0REREREwl9RDqz4FjoNr34l3PoEp2XewWm5s3UMwGmv1O1eUm/qORURERERkfBl2ybYXPaF6dWceF/19SNjwC4HlwsiatkX5x3MPn+Qkz/ucRh4at3bLPWi4FRERERERMLX3Nfh8xud485jqq8fGW3SpZ/BgJNq9wzvVXk9rAgYdkHtrpeg0LBeEREREREJX3Pf9D2OiKy+fnSiST+8ELYvrt0zvIf1eiS0hkj15TUlBaciIiIiIhJ+XC547TjYPNspO/6pmq9L7eTka7uljO3yLyur47xVaTAFpyIiIiIiEn5yt8K6X3zLhtdimG2EV2+nq7x2zwoUxBY3YK9UqRf1U4uIiIiISHhxlcPj/U2+x3hIbAubfjf7mNbEOzi1axmc7t3oXxadULtrJWgUnIqIiIiISHj5/i6TxreC8z+tXVDq4T0nNdBw3cqKcuCPl/zLM3rX/pkSFBrWKyIiIiIi4WXm0ya9ZVndAlOoNKy3FsGpZ06r5zrLHdymZNbtudJgCk5FRERERCR8rJth0ta9ISq27tfXdVivbZt05KUm7XWY+/jiuj9bGkTDekVEREREJHzMf9ukB1xXv+u9g9Py0prrF+eadNi5kNYZRl0GUXF177GVBlPPqYiIiIiIBJ9tm/1D70mFaY/4n8/ZCvPfhazVTllJvglOB54KIy6q33O955yWFdXcxs9uMPnWveCA6yE6XoFpiCg4FRERERGR4Hv3bHigrclPe8gsPORtyv3w6VXw3+FQWmi+HupozqV0rP9zLa/gtLyGvUo3/uZsGROTWP9nSlAoOBURERERkeCxbfjgAljxtW953g4nX1YCSyY7xw+2hy3znOODbq3/8xNaOfmqgtPSQigvc1YFlrCg4FRERERERIKnaK9v4OlRuNvJL/scSvJgvxOcslePNulpr0B8Wv2fn9YFjnvC5MuqCE4fbA/3tzY9pwDj/17/50nQKDgVEREREZHgydns5NsNhNFXmHzhHq86W0x6/JMQk+x7fb/jG96GgaeatLy4dvV7HNLwZ0qDKTgVEREREZHg+Oo2ePYAk7/mN7h6Boy5yhz/9rwZSguQvwsioiE+Hf6yxLk+vRtExTS8HZHue9Q059Sjy/4Nf6Y0mLaSERERERGR2pvxFHx/J1z5M3QY4nvu9xecfEZvk8anm3T1j2YorYdnuxbvhYhKa1hdt7Y8wWmgYb2uWux9KiGhnlMREREREamdmf8zgSnA8wcHqOC1BYtnS5e4tMD38mzz4r31S3RcQ1vovqc7zPnpYd/yNdPgxQnBeYYEnXpORURERESkZrvXwA93V33e5QJsk5/wD6c8oor+sEAr8rbpV+/m1cobJzbu/aVBFJyKiIiIiDRHrnL49v+g9+FQUgD9T6j5mvrK2QpPDTP56AQoLYC0rs5524b/us8f+RDsf03N9zzsTic/7i+wZ61ZIKmpRSc0/TMlIA3rFRERERFpTnYshZJ8yFoNvz0Lb50KH5xvejYby3+8ejSvngG9Dofs9TDpEtNjum467Flnzvc+wswlrYvD74bTX4O41GC1uPa857xKSKnnVERERESkuSjcA//bH5I7QO5W33N71kOrHsF/ZlGOk7/wc/MMT9mij0yg7FkY6ZpZzkJI1UnuEPx21tV1s+HpkXDADaFuibip51REREREpLlYN8Ok3oHpYXeZ9OfHgv+8Be/BrGdN/sy3obt7EaTTX3XqrPjGbA2T0Bra7lfzPWNT4ZJvgt/Wmti273FGb7hnLxxwfdO3RQJSz6mIiIiISHOxbjpmRVx3oDXkHDjgRrOH6Nb5wX/eJ1c6+c5jnHxKJ996+TshIaPq+1z7O2xbaLZ4acy5sdX5+q+By+s6BFkajYJTEREREZFwt2kOxCSYOaYARz0CtgvGuhceGnoO/Pp0cJ+5eY6TT+0MSW2c48oB3dLPoMvYqu/Vpq/5CgWXy6wY7L0Hq4QlBaciIiIiIvW1ew3kZ0HnUY1z/5ytUJAFLx3qlPU9Fva/yrdeZCy4Ss3Q1WD1BL54mJPfu7Hm+jFJwXlusNnlQAR0HQfrp4e6NVINBaciIiIiIvX10kQo2AX/2AlRMcG///MHQ/4O37Kz3/GvFxlt0vISiIqF0iJwlUFsPQLGnx+FVT9SMXQYoFVP/3pXTIOoePife7hvuK566yo3fz4J6YEXkpKwoQWRRERERETqq2CXSb2HwAZL/i7/wLQqUbEm/fhy03v61qnwz07VXxPID/fClAdgw0xz3Oco6HKACUQr6zgM2nptMROu+4W6ykxaVgxJ7Ux+4v2ha49UScGpiIiIiEh9FOx28tnrg3vv8jJ4tFJvZa+JcO5HgetHunttl0yG0kJn+Kptw7ZFcE+qSauzcwVM/49v2Zgr4ZKvIS6l6usOdi80FG4LC4272Z1x9wCXFUNUnFmh90BtHxOOFJyKiIiIiNTHmmlOfv7bwb239xxTgOOfhPMmQe/DA9eP9BpSXJLv5ItzYN6bJr9mavXPXD3FpIPPcspqs29qeleTlpfUXLcpJboXcLJdJi0rbpyh1xI0Ck5FREREROpj0sUmjYgycy+95W6HsjoEa9/fbXo3F31sjrcuMOlNi0xP34iLqr/eu9dy13Inn7MFsjeYfOXtXyr75naTnvCUU5aSWWPTGXAyDDwVhl9Qc92m5AmWc7e7j4vNwlESthScioiIiIjUUXzBFuegwxD3irDA7rUw9w34d1949oDa3SxnK8x4wuQnXQx7Nzvn0jrX7h5Zq5z8/Hed/I6lsPwrk7eq+dV/1rNOPioWLvgMDrsLImuxfmpMIpz2CnQ/uHZtbSqLPzHp93eatKzYmZsrYUmr9YqIiIiI1FHX9R+azIBTIG+7WR0X4LlxUJJn8lkra7e1y85lJo2IMov3eHpkz36/9g2yvVbWnf+Wk/fcC5yFgQL55g6T9j3GpD0OMV/NWYQ71PF830V7FZyGOfWcioiIiIjUUWyxe5Xe8XeYgKes0Bx7AlOPzXOrv1HWamdl3Mt+MOnG30ya1Lb2DUpuX3OdquaE7vLqdY1vVftnhjvv4NRVDjmbIa1raNsk1VLPqYiIiIhIXeTvIj37T5Nv09dsoZK7zZnb6G3Z55A5wr989qvwxU3OcfvBZmsWb+ndat+mMVeb+r89D+t+CVynMDtwuWdl37b94ZDbav/McOcJTsvLYLt7peK41NC1R2qknlMRERERkbrwrMx72N0mjUkyPaZPDQtQ12v+p8tlvgC+u9O33rY//a9NqEMvZmQU7He8E5hGRPkvULTpD//rykvNcFeAS7+rW0Ac7jzB6frp8Lx7PmxUXOjaIzVScCoiIiIiUhd71lEalQwH/cUcxyaZPU9L8/3r5m1z5qO+PBE+ON/kE1v71uvk7l098CaTXvZjw9oYnQCH3+tbVlKpfQW74f4M+P4ucxyT1LBnhpuIAINENec0rCk4FRERERGpi+yNFMW1cY5jEv3nmgIccL1JSwtMunk2LPvC5EsKYPiFTgB5sHs47aH/gGtmQebIhrUxOsG/l3DnMvNcj9ytvudrWripuQkYnKrnNJwpOBURERERqY1pj8A7Z8Kq7ymKa+eUb1/s5NO7mzQlE1r1NPmyImc4L8DybyB/h1nE6IAb4NyPoM9R5lxkNLTdr+FtLS3wDcSi4iF7PXx2vVPmGc7bUgUKTqMVnIYzBaciIiIiIjUpzIZpD8GKbwDYkz7EOWdFOvlxN5n0oJud4HDtz3BfulPn3TNNmjkaIiKg9+HB67X8i3tbmlbdzb09kt3B9OopTpn3Akmnvhyc54eTQHu0quc0rGm1XhERERGRmvz6lEmjE6D9YLa1P5Q+nnMxCU699O5w126IiIRFH5myT64MfM/ehwe/nSkd4KrpkOgednziM2ZrmgGnwJsnQbcDnbqFe0x6wzxo1SP4bQk1zTltdhScioiIiIhUxVUOv78Av/zbHF/0JXQajmvaNKeOd29cqx4mMK1c3pTaD3Lyw84zX2C2qvEszlReBpOvMfmkdrRI3j3aHuo5DWsa1isiIiIiUpVVP8A3d5j8fsf770UKMP5vTj4108lHVuql6zjc1I2Igp6HBr+tNYlNceaZbpjplMckNn1bmkKgodLqOQ1r6jkVEREREanKyu+d/HFPBA540rs6ee/zlasmtoHxd5ivUEhqBxtnmXz2BpOmZFZdv7mzbf+yiOimb4fUmnpORURERESqUlZo0ku+g8SMul3rvW0LOFvLhEpyO8jbYYI2zzYy188ObZsaVYDg1FXW9M2QWlPPqYiIiIhIZQW7YcdSmPcWxKdDlzF1v0dCK9/j7gcFp231ldTebGtTtNdsKxOfDtHxoW1TU0toHeoWSDUUnIqIiIiIeJSXwtd/hdmvOGVHPlS7aysvwNNtHFz0Faz9CTL6BL6mKSW3N2nedlg3HTrvH9r2NLbUAEOWvYdgS9hRcCoiIiIiArDsS3jvHP/yoQHKKrvkO0jt5F/e7UDf7VtCybMqb+422LMe+p8U0uY0uvF/h+mPO8fDLwhdW6RWNOdURERERKQ4zzcwPfxekx7/VO2u7zImcE9dOPEEp7P+B3Y57Fkb2vY0tqgYJ5+SCSf8N3RtkVpRz6mIiIiI7Nt+e94M5fUYeCqMuwl6jIcOQ0LVquDzzIFd8Y1Jc7eHri1NLUJ9cs2BglMRERER2bd5AtPeR8DJz0NcmjnuODRULWocUXG+x7YrNO0IhQiFPc2B3pKIiIiI7Lv2rDcLGQ07t+UP+6y8Mu++FJxWXqxKwpL6t0VERERk35SzBaY+CNhwyO2hbk3ji6gUoO1LwWlpQc11JOTUcyoiIiIi+56VP8Dbp5p8m/3CfzGjxjDsvFC3oOkU5YS6BVIL6jkVERERkX3P1Aec/IgLQ9eOUDnzbRh5cahb0fhOfsGklhXadkitqOdURERERFqmohxY+zMsmQxxKXDMY1CSD1/fDlvmwdjr4IgH9s3AJTqu5jotgWcos22Hth1SKwpORURERKTlKSmAhzv7lv3xEhz7b5j/ljnuMHTfDEwBohNC3YKmoVV6mxUN6xURERGRliFnC5SXmfyXfwlc58tbnHyvwxq/TeGq8rYyLVVFcKqe0+ZAwamIiIiINH/bF8N/9oP3z4WC3bDpD7Nf6d+3wFXTfetakfC3zZDQKiRNDSnL/et/5W1lWir1nDYrelsiIiIi0nztWAr/2985XvEN/Ku7yR9wPcQkQloX32uumAaxSU3WxLASnQAleftecKo5p82CglMRERERab7WTa/6XKcRJo1Lhf/bBh9fAT0OgQ6Dm6Zt4cgTnEZEh7olTaPy3q4S1jSsV0RERESap9VT4atbTf68j+EfO33P73eik4+OhzPfhFGXNV37wtHEe026rwxp9iz8VFYU2nZIrajnVERERESal8Wfwodee5MefJuzuNE9e2HDLBOMRqgfxs/Qc8zXviKhtUnt8tC2Q2pFwamIiIiIhI+1P5ttYPoe5X+upABeOAR2rXDKek2EQ//hW6/L/ogAkNg61C2QOlBwKiIiIiLh4/XjTTrkbIhvBUc95Jyb/YpvYAow8NSma5s0P7GpoW6B1IGCUxEREREJPwveNWlEBPQ+ArofDFkrTdnNS+CtU2DnMmjXP3RtlPCnod3NioJTEREREQkPb5/uX/brf82Xt9ROJljducx/mxiRysZeB+0HhboVUgsKTkVEREQk9Oa+CSu/M/mj/wWxKfDpVVXXP+JBOPBGiE9vmvZJ83Xkg6FugdRSjf3clmW9YlnWDsuyFlVx3rIs6ynLslZZlvWnZVnDg99MEREREWnR/nzfpLevgzFXwiCvXtSLvnLyp7xo0qgYSM1ssuaJSOOrTc/pa8DTwBtVnD8a6O3+GgM8605FRERERGqncA/0PMzpCY2MMtvCANg2dBoBvY/0DVpFpEWpMTi1bftny7K6VVPlROAN27ZtYJZlWWmWZXWwbXtrsBopIiIiIi1Y1mrYvggGnxH4vGXB5VOatk0i0uQsE1PWUMkEp1/Ytj0wwLkvgIdt257uPv4RuN227dkB6l4BXAHQrl27Ee+9916Nz87LyyMpKanGetJ49A5CT+8gPOg9hJ7eQejpHYReS3oHlquc+MKtJOavZcCSx/hj5JPkJ3ULdbNqpSW9h+ZK7yD0anoHEyZMmGPb9sja3i8YCyJZAcoCRry2bb8AvAAwcuRIe/z48TXefNq0adSmnjQevYPQ0zsID3oPoad3EHp6B6HXot7B/Hfh02srDkcdcTrEJIawQbXXot5DM6V3EHrBfgfBCE43AZ29jjOBLUG4r4iIiIi0RLvXQH6WST26jms2gamINI5gBKefAddZlvUeZiGkvZpvKiIiIiIVbBt++hdkrTIr7E7/j+/5TiPhnJqne4lIy1ZjcGpZ1rvAeCDDsqxNwN1ANIBt288BXwHHAKuAAuDixmqsiIiIiDRD89+GaQ9Vff7yH5uuLSIStmqzWu/ZNZy3gWurqyMiIiIi+7D57/iX3bwECnZBm35N3x4RCUsRoW6AiIiIiLRw5SUm/ctSpyy1E3QYAlGxoWmTiISdYMw5FRERERGpWt52GHQGpHSEG+ZBtBY+EhF/Ck5FREREpHGU5MOe9ZC3E5LamrJWPULbJhEJWwpORURERKRxfHIlLP3c5NO6hLYtIhL2NOdURERERIKvpMAJTAF6TwxdW0SkWVDPqYiIiIjULHsjxKdBTJIZrhuTCJZVdf2HOpi020GQ3lXDeUWkRgpORURERKRqxbnw/MGwe41v+REPQGomdB5jFjryVlro5M/7SCvyikitKDgVEREREX87V8Bbp0BRDhTv9T+/9hdY+a3Jx6XBUf+EoeeY46//atLuByswFZFaU3AqIiIiIr7ys+CZUb5lJz0HxTlO4OkJTAGKsuHTq6F1L7AiIXebKT/28SZproi0DApORURERMTXlPv9y4aebdIxV8I9qYGve9m96FGvidBhCGT0apz2iUiLpOBURERERHyV5Jv0zizYvbr6obl9joLCPbDxN6ds1ffQ5+jGbaOItDgKTkVERETE4XLB1gXQZSxERkGbvlXXzegDZ78H+bvgsUq9pCkdGredItLiaJ9TERERkX3djmUmKAXYsxZ2LYeBp1Zd/7rZMPY6uPgbs51MUhu4fR3csgI6jTB1kjtWfb2ISADqORURERHZl21fDM8eAAfeCOumw8hLTHnl7WG8ZfSGIx/0LYtPd6et3Ner51RE6kbBqYiIiMi+oKQAyoogoZVTlrPFBKYAc9+Ewt2weY45jkms33M81yVk1L+tIrJP0rBeERERkfrYvabqcwW7YdWPYNtN156aPHcg/Ku76R31ePUYJ1+427d+evf6PWfifTDoDOg6tn7Xi8g+S8GpiIiISF29dRo8NQzeO9cEoDtXwH2tYc7r5vybJ8Fbp8C6X+p238I98MIEp/cyZwvMeKrhQW7eDieYfu1YmPoQbJpj5pcC9D/Jt35qF0jvWr9npXeFU1+EuCq2mxERqYKG9YqIiIjUxqKPYOufJtBb9b0pW/YF3Jvm1Pn8Bhh+gVntFuD14+G4J8w1rXrA4NOrf8a6GbBlLrx9Bpz7Ibw4wZRn9Ia+9dyaZdtC+Pkx37KfHjFfAEf/y8wTXfKpc/6M1+v3LBGRBlBwKiIiImLb8OGFsGQyHHK7WRRo02zod6w5t+QTmHRJ7e61a6Xv8Rc3Ofk+R1Tfo/j+uSYt2OUEpgDvngV/XQtrptJx82/A+Nq1ZcMseOVI5zhzNGz63bdOfDoMOg3mvwVrppmy+s43FRFpAA3rFREREVnyqQlMwfQovnS4CRSXf2WCu0CB6fi/Bb7X4o+rfk7O1qrPrfy++jb+qztMuoQ+K5+vvp5HSQF8cKFz3GEoHPuYf734dLMdzDkfOmXRCbV7hohIECk4FRERkX1b/i748CLfsr0bTfr93f49jQCtepoe1luWO2WxKSad9s+qn1V50SFv3/6fSdv0c8qGnlt1/eqUl8KzYyFvm1O2awV0GOK/f6lnX9LIaKdMPaciEgIKTkVERGTfMPWf8HAXyN7gW75jqUnH/QWOfMj3XFalIboxSSY94gHT25jc3uwPOuoyOPtd37qDzzTpyS/AGW+YfGlB1e1r5V4d91yvHszkKvYKLS+t+j5gvsc963zLxlxp0tNegf/bDgfdYtrm2VrGspy66jkVkRDQnFMRERFpufZugqzVkJoJPz1syrYugLQuvnUAhpwNyz4PfJ+TX4Ceh0JSG9i7GVI7Oecm3ud7H48T/mvmrO53glmUCKC0qOq2luRDl7G+bTvwBtj4m/+qv3s3OcFsIIs+MmlcKhz1MAw9x/d8dBwcdlfV10fFVn1ORKSRqOdUREREWh7bhvvbwuMD4I0TYMoDzrklkyF3m1nBNms1fHqVKU/tBMMv8r9XfDoMOdMEpp56gSR3hDFXO8dRsdD/RNMjGR1vykoLA1+76kcTgO5a4VselwrH/tu/vmc14EBsG6Y+aPJXzfAPTGvDuxdVRKSJqOdUREREWp4V30J5sXO8+GNI7Wzmki780HwBTLnfqROT6DvX8vB7TY/nUdXMIfUWEQFHPww9xkNyO99zUXEmLasiOPVs41KQ5X/Oc623Dy+EAXsD38uzd2lUvOkxrou+x8LyL+t2jYhIkCg4FRERkZbn3TP9yxJaQXyaM8S2JuNuqt+z+x7lX+aZwxloWO+Kb2Gue06qZ7Giq6Y7gXJihlO38/6wcVbVz7ZteGqYyR92V917QM98C7Drdo2ISJBoWK+IiIi0LNMfd/I3L3Hy0YmQ0SfwNd6r7t68GG6YF9w2Rbt7PwMtiLTqRyd/2ismbT8IWvUw+ZhE+MdOGHMVnP4aO9ocWPX3keu1VU1EZN3bGRFRv+tERIJAwamIiIi0HNsWwg/3mPwV03znh8YkQGxy4OuS2zv51EwnMAyWKPec0zJ3z+mK7+CTq01P544lVV9XcX0MHP0IpHSgPDIOivMC1/MOTrse0LA2i4g0MQ3rFRERkZbBtmHytSZ/ywpn3qcVAbbLDK2tKjhtbBHu/oBp/4R2A+Cjy83800P/z1mJ94qfanUrV0Rs1XNXPUHrif8zva8iIs2Iek5FRESk+fn9RdP76C1rlbOKbVJbp3zExSaNSYTYlKZpX3UmX+sEl1vmmzQlEzoOrdXl5ZFxVa/6u2SySdv1b1ATRURCQT2nIiIi0jys/AHePtW37G+bITbJ5PN2mHTAyb4LAaV3NWnRXhh9uempXPsztOoJu1fD/tc0ftu9FXmtsvv+uSY98elaX14eGWuGB7tcTo8swJ71MPtlk48JUQ+xiEgDKDgVERGR8Ldlnn9gCvDOGXDxVyafvd6k4/7iW6fnofD9XaZXNT4dLvwc5r8D3Q82x575oKGU2rnWVV0RMSazegrEpcC8t+DQO+E9r/1MvbfEERFpJjSsV0RERMJH7jYzd3TnCvj2/6CsxJS/fqJJx1ztW3/9DCc/9SGTJrbxrZPiWRTJqzd16Dlm4aOYRN/ex1BJaFXrquWRsSbz9qlmZeK5r8OaabB9kdf9Wge3fSIiTUA9pyIiIhIecrbCf/r5lpUWwHGPm61Y4lLg6Ieh79Gw6XeY8oCp89w4uPR7cJWZ45QOvveIT4fxf4f+JzT+91AXQ86GBe+afB0WanJFxDkHnrmniz92ys58y6zuKyLSzITBR4UiIiIiwIaZ/mWzX4HCPWY+6YgLTVmPQ+Dg25w62xbCV7f6bqPizbJg/O3Qdr/gt7m+2g+GLmOd48joWl+6K2OUc7BmqkmXu4c29z4S9js+CA0UEWl6Ck5FREQktGwbXjwMJl0c+Pyc1wAbuh7oW37BZCfvWfV2/N8aoYGN4MAboU3fel1aFl3NisODTq9ng0REQk/BqYiIiITOht/ghfGwebY5tiLgmlkw/EI46BZTtn2JSTNH+V7b1mu7FM98y47DG7W5QZHaGQadBgkZwb93Upua64iIhCkFpyIiIhI6X9wEW+ebfHp3+PsWM/z2hKec4G3hByatPPQ1qS2MvsK3zC5vzNYGx6XfmzSxAYsWdd4/cHmbMBq6LCJSRwpORUREJHTytjv5y6dAtNe2LqX5Tn6I1zYp3o54wPe4+yHBa1tjiXQvVhSXVv97HP+Ek49LddKktvW/p4hIiCk4FRERkdCY9SwUZJn8We/4b6cy6Awnf9idge8RFQsR7s0H4tMhJiH47QwWy/1rV6S7vZYFE/4Pzv+07vfyXtxp4n0mtd33FBFpprSVjIiIiDS98jL45g6Tv2OD0/vnLb0rZPSFXcsDn/eIS4OCXXXajiUkOo00W+BEeA1PPuSv9b9f/5NMMNp+sDku3tug5omIhJqCUxEREWlaJfmw/GuTP+D66gPPK6bCrhUQk1h1nbQuJjj1BGnh6twPYOufwevdPeP14NxHRCRMKDgVERGRxuNyQYTXLCLbhoc6mnxSezj8vuqvj0mEjsOqr1NWZNJ2A+vfzqYQn272aBURkYA051REREQax+opcF86ZK02x9sWwgPtnPOlBb6Ba30V7jFpcW7D79VcDTsfJt4f6laIiDSIek5FREQkeBZOguIcGHmJWfAIYNUP8N8RmBV7vBTnBOeZxz8F75wOHYYE537N0YlPh7oFIiINpuBUREREgmPvJvjoUpP/4man/OsqFv258PPgPLfPEXDlL74r2IqISLOj4FRERESC4/cXaq4z6jKzZ2nmiOA+u0OYL4YkIiI1UnAqIiIiwTHjyerP/2OH2ZdUREQkAC2IJCIiIrUz/XEGLHoYPr0GHh8Ia6aZ8uI8ePUY//o9xvseKzAVEZFqqOdUREREarbxD/jhHtoA7Jppyt44Ee7ZC9/cDutnmLKLvoTsjZDRxwzdXT0V3jwpRI0WEZHmRMGpiIiI1GzxJ/5llnsA1s4VJo1OgG7jfOtExpi0VY/Ga5uIiLQIGtYrIiIi1XOVw6JJ0P0QCuI7OuWRMVBeCtv+hM5j4Jbl/temdzPp4fc0RUtFRKQZU8+piIiIBFacC0V7zSq8edvhkL/ye5eejI9ZCEs/g01/QOEeKCuC/idBXIr/PVI7wd3ZYFlN3XoREWlmFJyKiIiIv69vh9+e8y1L7wb5Fhx4A5SXmOB05ffmXFLbqu+lwFRERGpBw3pFRETE1xc3+wemAO2HOPmoOJNOvgYioqD3EU3TNhERabEUnIqIiIiv2a8ELo+Od/IdBjv5lI6Bh/SKiIjUgYJTERERcdi2kz/8Huh9pHMcneDkux3k5MvLGr1ZIiLS8ik4FREREXC54LmD4N40czz0XDjgRjj7XTjsLrh+LkR4/dpgWTDxfpMvK2zy5oqISMujBZFEREQENsw0W8J4jLvZCUYPuiXwNZ1GmLRwT+O2TURE9gnqORURERF47Rjf44zeNV8Tk9g4bRERkX2Sek5FRET2dcu/dvJt9oPWPWt3XbsBMOBkOOjWxmmXiIjsUxScioiI7OvWTTfpBZOhx/jaXxcZDae/1hgtEhGRfZCG9YqIiOxr8nbC0s+d4/ydkNqlboGpiIhIkCk4FRER2dd893/w/nmQtdocF+6B+LSQNklERETDekVERFqqsmLAgqgY3/Kdy02avR7i02Hld03eNBERkcrUcyoiItJSfXgRPD4ACrN9yxNam/TNk2H2K03dKhERkYAUnIqIiLQ0JQUw901Y/hXk74BHukJpoftcPqz+0ak75X6THvd407dTRETEi4JTERGRlubbv8Nn1/mW/fmBSSe7y7se6Ht+xMWN3y4REZFqKDgVERFpSfasgzmvOseeFXhdZSaNSTDpuR/6XmdZjd0yERGRaik4FRERaUmmu4fndt4f7toD57h7THcsNQsk5WyFdoMgJhHuzApdO0VERCpRcCoiItLcTHkA3j4dysv8z+VsNSvwXvgZRERAVCzEpsAfL8KzB8Dm2dBpuKkbqUX7RUQkfOinkoiISHNSnAs/P2ryD7aHO3dCaQHYNsQmQd42aDfQBKUV1+SYNGuVSXsc4pw76VmITmiatouIiFRDPaciIiLNycxnnLyrFP54CR7tBf/sZMqyVkNKx+rv0e1gJz/0HBhwUtCbKSIiUlcKTkVERJqT5Pa+x1/danpOAfJ2QEme6Tn1dttq32PPPqciIiJhRMGpiIhIc2HbMPUhk798iv/5ZV+YtNdhvuWJGXDqyyZ/yktmLqqIiEiY0ZxTERGR5mL9r5C33eTb9PM/v2MpxCRB2/7+5wadBgNOhojIxm2jiIhIPemjUxERkeZizzqTnvWO2QomJdP3/O8vmIWQqtqzVIGpiIiEMQWnIiIizcXcNyAhA3pNNMepmf51Bp/VtG0SEREJEg3rFRERaS52r4Z+x0BUjDk+801YPRV2rYBfHjNlRzwQuvaJiIg0gIJTERGRcLZ5LkREmVV483dCzlbnXFJbGHIm/GeAU6bFjkREpJlScCoiIhLOXpzge+yZd+pt4r3w0aVN0hwREZHGoo9XRUREmpP9r/Iv63+SSS0teCQiIs2Xek5FRETC0Z518PNjvmXHPwXDL/CvGxkFp7wYeAsZERGRZkLBqYiISDjZvgQWTYJf/u1/rk2/qreJGXxG47ZLRESkkSk4FRERCRe2Dc+Orfp8235N1xYREZEmpjmnIiIi4WLDzOrPx6U2TTtERERCoFbBqWVZR1mWtdyyrFWWZd0R4Px4y7L2WpY13/11V/CbKiIi0sKtnmrStK4mvWs33LPX5Fv3Ck2bREREmkiNw3oty4oEngEmApuAPyzL+sy27SWVqv5i2/ZxjdBGERGRlq0wG3K2wKbfocNQuPIn3/N3bITI6FC0TKRZ2rSngFU78jiwVwbRkU5fzM7cYqYs245tQ1pCDEf0b0dERBXzuIG5G/awK7e44rhVYgwju7Vq1LaL7MtqM+d0NLDKtu01AJZlvQecCFQOTkVERKQ+Hh8IJbnQZj9I7+Z/Pi6lyZskEm7KXTY3vDePrLxiv3M92iRx02G9sSyLyAiLq9+ay8LNZtRBhAU2Zkp3ZZnp8WSmxwd83t7CMpZuzfEr/+y6A2mfEkd2kYtyl7npluzCivNRkRbtU+Kwqlq8TESqVJvgtBOw0et4EzAmQL2xlmUtALYAt9q2vTgI7RMREWnZvvuHCUwBdi6FIWeFtj0iYerezxfz5Z9biY+OZFCmM//697W7mbVmN+/8tsHvmmsn9MTCIsKC/JJyZq7OYmiXNK6b0IuXflnLoi17cQUIWgGS46IY3a0VV43vQdvkOJZuzeG2SX9ywtMzKupMzZ5PekIMr/26zufaf502mDNGdiavuIyr3pxDTlEpcVGRdG2dwEOnDPLpza1s3oY93PrhApJio3jzsjGkxGnUhOw7LDvQx0jeFSzrdOBI27Yvcx+fD4y2bft6rzopgMu27TzLso4BnrRtu3eAe10BXAHQrl27Ee+9916NDczLyyMpKakO35IEm95B6OkdhAe9h9Brae9g2NzbSc1Z5lP22+jnKEzoEKIW1aylvYPmKNzeweNzilif4/IpO6JrFMf0iPEpm7G5lK35NpEWDGsbSbfUyGrv67Jtissh0jK9n5d/V8CwtpFcMzSWKK+huPmlNnO3l1HmAhemh9QGhreNpHV88Nb+dNk2v20tp6jM/O48ZX0xWwtN4Nsq1uLYHiaIfGVRCcf1jObU3jGs21vOPTOLfO5z5/5x9Ewz33u5y+a3beUUlJp7FpTZfLyytKLutUNj6ZzsfA/psRaxUeqR9Qi3fwv7oprewYQJE+bYtj2ytverTc/pJqCz13Empne0gm3bOV75ryzL+p9lWRm2be+qVO8F4AWAkSNH2uPHj6/x4dOmTaM29aTx6B2Ent5BeNB7CL0W9Q4+uRo8gWl6N9izDoAxx5wdsibVRot6B81UY72DvOIyJjw2jd5tk3jn8v1rrF/usskvKWPBN98xvEsafdsnm/Yt38m3G8vZWBZXUbfM5eKPdfkVxwuyo7l5Ym/KXTard+ZRXOoEt+W2zY6cYr5etBWXbbb2bZ8Shw2cNLYfh4/t5teWY+v/bdfJoV75dh/9yLISM//0mEEdOGpgewDevesbPl9dyiMXHkry1hyY6bsK92eb4+iUH0d+cTk/rdgZ8DmDOqWycPNenpnvP4T5ruP6A56hyk4nkydrY1cc217nvMsBemQkkhgbRavEGAZ2ap4rgev/o9AL9juoTXD6B9DbsqzuwGbgLOAc7wqWZbUHttu2bVuWNRqzCnBW0FopIiLS0ix4x8lf9iM82jN0bRHBLBbk+arJH+t2c86LsygtN5HOZQf14JhBpsf/47mbeO/3jRXzMQEsLA7o2Zo7j+vP+qwCrnprDje+N7/ifGKMby9qemIMvdsm07tdEm2T48guLCEmMoLD92sXhO80OPq3juSa8cP8ysf2aM2Py3awdW8RxWUm6D51eCaWBTmFpazdlc/K7Xms3JEHwJkjO/PXo/pWXB8XHUlCTCRTlu0gt6isovzt39bzx7o93PdF8Jd9+f3vh9E2Ja7miiKNrMbg1LbtMsuyrgO+BSKBV2zbXmxZ1lXu888BpwFXW5ZVBhQCZ9k1jRcWERHZV636wcn3mACJGaFri4hbWbmr5kpus9ftqQhMASb0bVuRP2V4JqcMz6zy2v06pDD99gmUuAO3VokxpCXEVFm/uTl7dBd+XLaD/OKyij+jc8Z0ZkRX31V+F23ey7JtuZw8rBORAVYMPqxSIH7SsE7sLSjFxsbCXd8yPcvubMUiTJ67WRYVdb3XZyopd7Fyex6z1mTx6LfLycovUXAqYaE2PafYtv0V8FWlsue88k8DTwe3aSIiIi3UtEfMUN5rf4eoWFN20VcQUasfyyKNwjvYnDx/MycM6YhlWezILWLtznyfunPW7yElLoqjB5rhrPEx1c8frSwzPSEobQ5HqQlm7uklr/1Bn3ZmqHNMpP+fz8BOqXUeTuu5d0PFRUcyoms6BSWmZ/a2SQtwuWDNrryKHm/LsvjnyYM4dUTVHzTU18vT1xIbFcF5+3cN+r2ledNPQRERkaa06gezn+n+1zqBKUC3A0PXpmbMtm2mr9pFfnE543pnkBSrX23qq8zl9Jze+N58nvxhJX3aJfPN4m0B6x/QszWPnDa4qZrXbAzvks6tR/ThwzmbWJ9VQP8OKXRpFZ7B+MCOqUzo24b84nIADu7dhl5tzeI2b81az8w1WX7BaXZBCV8t3Ea5bdMpLY6hndNplVi7nu+SMhfPTF3Fkz+uBFBwKn70P7iIiEhTWfsLvHWqyXepecEZqdmybbmc//LvANx2ZF+undArxC0KPztyi/h64TYO7tOG7hmJAGzPKWLKsh2Ulbsoc9nYNnRMM/t9/uPY/fhg9kZcNqzdlU+/9smM65XBof3a+ty3j3sBJPEVGWFx3aG9ue5Qv40rwk56YgyvXjw64LnZ6/cwac4mPpm32ae8PMDeO1cd0pPhXdI4YkB7v3MrtucSExlB51YJ9PnH1z7nduUVk5EU63eN7LsUnIqIiNRFYTYU7oZWPep+7evHmbTP0dD/hKA2a1+VXeBsu7Fxd0EIWxK+3vh1PU9PXcVxgzvw9DnDAXh6yirenLXep97obmZO5H4dUvju5kOavJ0SXm4/qh9Tl+0IeK5zq3gGZ6bxybzNvPDzGp77aTVgAvNyl02EBfHRkRzStw1fLTQ9756/X95u/XABr1URHMu+ScGpiIhIXXxxEyz+BI79N+xea4LVZV/AeR9BZjVbubnKnfyoyxq7lfuMolLnz/W9Pzbyt6P385uX98m8TWTllXDZQfX4QKEJvfTLGnq1TWJ837Y1V66DPQUlAPy5aS//+W45ALPWZNEjI5EPrhpLpGVx9JO/8Pu63QBERwZvb1BpvkZ0TWdE1/Rq63RKj6ewpJxBmalsyCqgpNzFW7PWkxQbxY7c4orAFKj4+wUwJDOVLXuLarUytOxbFJyKiIjUxR53b9OXt/iW//oUnPFG4Gv+0x86urecOOJB6H1447VvH+MdnAKMeugHRnZNJyoygqgIi6gIi++WbAfMdh7ptZwbFwoPfLkUgHUP++/aads2BSXlJNZjTq1nO5KNewr479RVFeWnDc+sGFJ534kDmLNhD0kxUQzp3Dz3vJSmlxIXzf0nDfQp+/sx+wFmbmpaQgy2bbNxdyFb9hZy1guzAHjxwpE8+OVSvl64jaOf/AWA0nIXq3bk0ck9vNxbVKTFcYM7cO2EXiTEmH8Dm7ML+Wx1CQvLV3LM4A70bJPUmN+qNBEFpyIiInURnQAxyVCS61seW8X8O5cLcjabr+rqSb0UuoPTg3pn8MvKXZSUuSgpc5FfUk65y0WZ1wq0H8zeyJWHBHc/2XKXjQVEBNgKpCq2bZOVX0KrhJiA1z3140pioiI4ZXgn2ibHuctW8fgPK1hw1xG1ekZZuatij809BSUMzkzls+vGVVn/iAHtA84XFKkvz/ZAlmXRpXUCXVon8O1NB9M9I5GYqAhOG5FJQYnz4dL37g+RMtPj6VxpAall23J4Zupqnpm6umJ4cEVP7MoV/Pv7Fdx9fH8uPrB7E3xn0pgUnIqIiNTW3s2wfjp0GgGb5/ieyws8N4vNs32PFZwGVVGpCcAeO30IrRJjiIqwKvZ69LBtm+5/+4r8kvJAt6i3T+Zt4i8fLCDCsvjnKYM4Y2TnWl33yDfLee6n1Zw9ugsPnTyQj+duZvXOvIrz//l+BQCxURFcOLYbb85azzPuHs/d7iG6u/NL+HnFTmyc4Nu24fWZ6ylz72FZ4rVv6UG9tZeuhF5fr0W0DurdhoN6t6k47nbHlwBcM6EXh/Rp43ft4Hu+JaeojKz8YtomxzG2R2vaReRw+OiBXPfOPGauzlJw2gIoOBUREamtj68waUJr/3Mrv4OvboOUTjDuJqf85Ym+9eJSGq154Wjdrnxemr6GGw7rXdELGEhxWTnfLNpW0dvn0a11IqO7+y+k4pGVZ+asxUVHVjlX0rIsYiIjKKl07+q8OmMt3yzaRmSExR1H96Oz176cecVmmOz7f2zEtqHctvnrpD/JTI+na+tEPp23mV15gefSRUdGVKx++u7vG/h28TZ255uAMzYqgn+fMYRyl82N783nX98s5491u33m7blsE4w+99NqXvh5TcBntE+JY0K/NvRum0xKvPlVb1wv/1/2RcLR2B4B/n8FJl83js17Chnn9UHLtGnTGD+4Iy9PX1sxikKaNwWnIiIidTX0HDjkDmjdE144BPasM+W/v2DScTeZxZI+vcb/2tQuTdVKPztzi7GxaZMUi2VZ7Mwt5t3fN7Alu9CvblSkxZUH9/QbXldXX/y5hbdmbSC3qIyjB3YA4LVf1zJ73R4GZ6ZyQE/zi+YPS7ezbFtuwHt0a53Ao6cPYZTXap8lZS5O/t8MFm/JISrCIj46stp2xEbVPjjNLy7j6SmryHIHjSc8PaPKujcc2ovWSbHc/dliznnxN59zyXGVfs2yIdcd2HqM79OGTunx3HBYb5/g+l/fLGdzdqFPYAqwPiufCCC3qJTWiTF8dPUBPuejIi06pcX79R6LhLt5d07EsiAmKvCHTN0zEiu2QqosISaSOev3cPx/p9fpmRERFrcf1bfi/yEJPQWnIiIi1cnfBdMehkGnQ2IGpHWBASc7529cAHNeh89vcMqKc+G/w8EOEAyl1W7oZ1155j5aFn6Bictlc927cysCnUdOHcSpwzN5+7f1PPHDStITon1+ISwrN3Mi35q1gROGdKRNciy3H9WPyAgLl21j23atgx/PloiT529h8vwtPufmb8xmwaa97no26QnRfHzNgURHmnuv2ZnPbZMWsC6rgNOfm8m/Th1MK/eCRnHRkSzeksPBfdpwyYHdqvyF1iMmKoJl23KYPH8zCTFR7N+jFclx0QHrfjx3E1n5Jbxy0UjKym2f4H3ygi3M25BNzzaJnDi0E+ft35XU+Gj6tk/mlelryS4o5aIDuzG+b5uKhVs8PMOLAf59+hBOGd6pyj/H724+GMuC+z5fwnt/bKR32yRW7shj2bZc+gPFpS4SYiPpVsUv6yLNTUMWKztzVBdio6r/gCqQact38MvKXc0uOF27K58Jj03j4D5tePS0wbRLqXpUSnOj4FQkFPZuhj/fgwNvgoi6/2cqIrVQXgZlRRDbwBUcl30Bf7xovsDMN62s8r/jZV85gWmviXDeJHignWlPtP9KlA314s9rePCrpU5zLLPfYIRlYQOnjcisCEw7psZx+0cLuf2jhQCkxEUxL8AiO9e9M5fFW3L4bIEJKF+evtY5+a0JsCwLLOC6Cb34yxF9WbE9l398sohWiTH847j9yExPqFhN96sbDvK5f7eMBL/grbLM9ARm/e0wFm3O4cwXZvLXj/70q3PKsE612nqlU3o8v67O4tfVWQBcO6Entx3ZD9u2+XnlLmIiI4iPMe9x6vKdABzSpy2RlRYsumBsN1buyKN32ySfxYz279Ga/asYjujhHYjGx0RWG+B7VuWNc/cIX3xgd16avoa567Pp3xV25hUToy1fRAA4YUhHThjSsc7XDb7nWwqDPBe9Kfy2xvw/9vOKneQWldGuBc0WUXAq0tRsGx7vb/I7l8MpL4S2PSIt0eop8Ka7d/PubBNF1ceedfDFX3zL9m7yrzfkbIhJgvyd8NWtkOVs18HZ75r0uj8gb2f92lGDt34z29vcdHhvXLbpKS23bdbuzOebxdt457cNAHx41VhW7cjj7d/WExcVyfi+bRjQKfC2IU+fMxwwW7W8OXM9haXl2DasXbeWrl27mWV4bJsPZm+q6P38fsn2ihU0oyItnj5nOPM2ZBMbFUH/jvX77cmyLAZlpvLjLYewK9cMs91bWMp5L5shtDX1mHp8cOVYduUVU1Tq4ryXfuPd3zfyy8pdbNhdQHZBacBrKgemYIYBei/qUl+1DSxvntgHgFOGd+LrRVv5Yel2Nm2PYFdJaY3BvYhULz4mkklzNvGPY/cjqhl92OP5kTbjjkMDbr3TnOl/NZGmVpDl5Jd+DqWFUFIAidV/4i4itbT+VycwBSjcAwmtYM5rEBUPQ86s/b1+ewHsckhqD3nuuX8nP+dfLyISBpwE892B6I4lJr1uDkS6h46mdTFfDTBj1S5GdE2v6E0DmL5yF+uzCgC46fA+ftds2lPAbR/+iWXBfh1SGNWtFWePrn074qIjufzgHhXH06ZtZvx45zlzNuxhwaZsZq/bzYKN2bRJjiUzPZ4py3Zw8v9msHRrDl0aOG8VoENqPB1SnV/CfvnrBB7/fkW1iyVV/j4y3YsaXXlID35eYT4oaJ0Yg2VZnDWqc8Wcz0/nb6ZjI//CV9ugOjU+mntOGACYPSUBlu120aVVFJnpLeuXUpGm1joxlu05Oazckcd+HULT/ZhbVMqVb85hb2HgD8kCWbwlB4C0+MBTE5ozBaciwVBWDO+dY3pCb1xQ/VDd+W87+dICeNC9r9w9exu3jSL7ileP9j3O2w7bF8HnN5rj2ganxXmw8APoOg4u/hLKSsyw3OpW2/UM2V32BXQYChm96tz8qqzakce5L/3GWaM68/CpgyvKf1lVfW9sZnoC716xf9DaUVnPNknMWJXFac/NBODAXq256fA+PD1lFS7bZmyP1tx+dL+gP7dzqwT+c+bQel178YHdq91yYkK/mocJ11daQjTZBaW1Dk69HdQ7gy8XbgWgtNzV4npMRJraX4/qy0Wv/hGylX535RXz0ZxN/Lo6ixFd00lPqDnYXLMrvyKfENPypoYpOBUJhg0zYdUPTr5b1Rud8/1dTdMmkX1RoCG3y76E3V5bbpSXQWQNP/5Ki+CxPlCaD4ffY8qiYsxXdbznk3YJTkDoctk88s0yFm0xH2C998dGnxV0n//JfG9PnzMsKM+rq78fsx/HD+lIUWk5RaUuBnRMoWNaPK9fMjok7Ql39504kM8XbGG/9nXvpTlrdBfW7srnlelrKClz1SvAFRGHZ5XvJ35YSa82SfRrn8wZo4K7aN36rHyKy1z0aec/HeDm9+fzy8pdgFkkrTYLnHnmybdKiGmRq3IrOBWpqykPwLaFMPxC2PArTLwfVnznnJ98Hdw43/+60iLTu+px4jMw+dpGb67IPmXXSid/ykvw8WUw5X7fOkV7qx5Gb9vma+qDJjAFSK3DLyrdvBb9SW5f++uqsTm7kOd/XkNrr5UsH/12uV+94wbXfTGQYIiLjvTZ4kWqV9+FWzyS46IodZktaRScijSMZ4Xgn1fs5OcVO4mKsDh9ZGZQg75DHp0GBF76wLbhkgO7c+EBXenaunYrb1uWxSF9Wu6+xQpOReqiOBd+ftTkV3xj0szRMOsZp07b/oGvXfENrP7R5Mde5/+L63vnwlnuIb/rZ8KrR8EN86BVD0SklsrNgjlcPhU6DTfBaWWl+UCA4HTdDHjtGDj4r/DrU065XYfhXjEJ0OdoWPE1JNc/AMkuKOGTeZspd9ms3J4HwP0nDeSoAe0p8+zN4tbnH1/X+znS/Hh6zUvKXLRvQdtHiIRC77ZJvHfF/hSVlrNg414e/2EFecVlVW4zVRPbtilz2URFWH4BboeUOE4bkelTFhMVwQUHdKuYTy4KTkVqb+pD8NMj/uUfnO97vPanwNfHeH0iNvoKyF7ve37ZF07+3bNMum2RglOR2nr3bNi6wOSjq1mAZ800GH6Bf/nSz0w6/fGGtcPT45rcrt63+GD2Rh76aplPWXxMJBERFjGVVpD96OqxbM8prvezpHk5YUhHyrYuZ/SY/bUgkkgDWZZVsQXUpj1mP+Mh937Hs+eN4MgBvp0Ib/+2nl9W7Ap4n6S4KO47cQD/+HQRH8/dTKvEGJ/pFwBDu6TxlyP6NsJ30bIoOBWpjbLiwIFpICV51Z8/4gFI7wq526quU5Rt0tJCeOdMElKPrd2zRfZVy76E5V85x9UFhos/8Q1Oy4rhh3th0Ufm2OW1YuKw86DHhLq15ZjH4Id7oNPIOl1WUuYiK98EmSu355ESF8WMOw7lxV/Wsml3AcM7pwe8bkRXDandl1iWRev4CL9ffEWkYY4b3IHCknIe/mYZf27K9glOP1+whf/7ZBEAfSvNHS0oLWPj7kL6tkvm47mb6ZQWT592SXgGufRqm0SrxBjuO3Fgk30vzZmCU5GaLP0c3j+v+jqpXaDXYbB7Naz9GQp2gxUB8WlOnVKz1UPFL7qe4R59j/H9pdrbJ1cAkNG9NRCgp0dEYPGn8OGFznHb/hDvDuTSu8OetXDzYoiKg0d7mj1Qy0udLV42/u47NN/bqMvrvkdqm77O3qZ1cOnrf1QsjAHQv0MKyXHR/GWi//YwIiISXGkJMVx+cA9emr6GZ6auZnd+ScW5+RvNgnRTbx1P90qLFs3fmM1Jz8zgwa+WAnDGyM7ceHjvpmt4C6PgVKQ6ZSUw53XneNAZMOxceONE33rnfgBt94Ovb4etf8K/3FsUeLaH2b7YLMICZk4amF6Vo/8FQ8+FL/8CG2aZck/qJcJV+72vRPYpeTucwDQiClxlvkPoL/rCBJ+p7nk++x1vPnDauRzauz/FzlpJlaobHhxE2QUlzFi1i2MGtefg3mahiyGd05rk2SIi4sgtKgPgq4XbiPVadOzEoR39AlOAzPR4EmMiyS8x6xP0aZfUNA1toRScilTn06th1ffO8SkvQOEe/3qe1TyjYs0+iB75u2DJp/DlLU6Z55fdiAgYc6VTlr0epv4TZjzpd/vY4t0N+z5EWiJXOTzm9en06a+ZUQ6xXkOuUjOdwBTgsLtNcLp1gROcLvWa712Z9+iHRrQluwiXbeYTHjWwQ5M8U0RE/B3Rvx2fzt/CpKvG0jvA9i+VZSTFMu+uIyhzudi4u5DebRWcNoSCU5GqbJgFiyaZfFQ8XPOrGd7n+cV31OVQuNvMU/MEnFFxvsHpixMge4PvfWMCLBXuKfvp4YBNSd27pAHfiEgQLPoIdq6A8XdUPczVVW7mScc20Q/mhR/6HmeOMukB11d9Taue5t/r9kXw0eUw5EwoyHLOR8aYvUpPeg6S2pqvJuDZAD4uuuVtqC4i0pz8+4yh3Hlcf1onxdb6mpioCGKIoG/7moNZqZ6CU5GqLPCaM3bCf51VcyOjneG6ZSVw1COmFxTML7beKgemA07x7dXxCBSwekko3GJ6YRMz6vANiATRpEtM+tPDcN1syAgwn+bLW2DOq3DXHhPANvbm4FmrTTr2Ojj4VjPP1PNvsyoREebf8opvYPcaWPiB7/leE+HsdxqnvdUoVnAqIhIWIiOsOgWmElzavVmkKivdw3lvXw+DTw9cJyoGkrw2Qi6q4RfjUQH2XATf4DQuzaSpXUyaOdqkW+dXf2+RxlJeac7za8dC9kaY/apv+Rz38bd/g/8Oh9IiGk3eTlj/KyS2gSMfdBZAqo3UziYwDeTAG4LTvjrasNssmBav4FRERPZhCk5FAikrgZwt0P2Qus05q257GICUKuaSefemFmWbILbv0eZ4wEkm3bGs8lV1l7u95gBa9j15O/wDUG/5lfZ1y9sOTwyEL25y5mBv/N05/9tzJvjznq8dTGXF8FgvWD/dBKd1Vd2w4/Ru9W5WQ9zx8UIAUuK1EbuIiOy7FJzKvmvBe/DWqc7QQG+7lgO2716IteG98Iq3tK5mDlt698Dn2/TzPY6MgcPvhuOfhBEXm7Ly4rq1pbLcbfDvPvDxlQ27j7QshXvMokJTHvA/99Oj8NsLMPnaqq8vc/+9zFrlf27bwuC0sbIlnzn5XdWstFuVohz/suvmwD92QnJ7/3NBVFxWHvDLIz1BwamIiOy7NOdU9j1lxfD5jc6c0snXwiXfOOdtG776q8m3H1y3e4+/A6b/x788qS0MPbvq6zoM8T22bTPUd8RF4HK5213id1mtTXsEpj1k8mt/qv99pOX5/UWTzngCcreaD0Si4+G7O+HXp8w5zxDz0VfAn+/79r57FgALFPCV5DdOm3M2Ofnxt9f9+vwdJk1obRZDGvcXyOgVnLZV4+kpK3nsuxVVnj9//66kJcRUeV5ERKSlU3Aq+57PboA/33OON8w0AWuUe/L7/Ldhw68m37pn3e4dFQsT74fv7/Qt731k9dfFJMLVM+HZsea4KNs5FxGBy4oior49p7nbnMAUzNy8shITVMSl1O+e0jKUFcPUB53jP98HKwJOfs4JTAH2boBh58Mxj5q/3w+2870HBB4uXtZIc07zd0F0Ivzflvpdn94NtswzK/cWZJlVtpvA8u15tEqM4dJx/iMooiMtTh/RuUnaISIiEq4UnMq+5ckhsGedc9z7SFj5rZlvt34GvHmyc+74JyGiHouTHHA97H8N3N/aHN+yonbz4tr1d/LFuT6nIuwymP64GYp8Sx3nnlYeblm4Bx5wt6emlU2lZfv5Mf+yBe/Cse7e/7YDYMdik49LNWl0HBzzGHx1qznO2Qxt+kJxDsQkQUmec69GWBApZe9ymPd03RZAqswzXP6Pl8xxdOMHp0u25DBn3W7ap8Rx7YTG76UVERFpjjTnVFo+lwsWfQz3pPoGpuBsh+Eqg81znPLuB5shtfVhWRDp9blPcjtnq5naKi0MXJ67te7tqRyAlBbU/R7SMu3daNLBZ/mWz3vTpMlePaSehY/ALNh1xTSTz3H3XhZlQ2wKnP66U897/9Ag6b/kUZNJbMD+o3Gp0OMQZ9hxfKuGN8xtyZYcbp/0J/d+vpgpy7ZXfJ34zHS27C0iPkar8YqIiFRFPafSMu1cAc+Mco7HXO17/pjHYNBp8OeH5thVZuZ5erQOsIdjU7hlhVm0qFJgvDdlP1JzlpqD8lKY/gTsf3X1q44CFOyGNVMbpanSzOXtML2kPQ+FLvv7DnVf/rVJD78HDr8Xnj8Iuh3knLcsaN3Luc/G32HeW9BmP7O6dI91MPm6ui1WlLvdfHjUZUw1bd5JXPFOk/eeJ15foy83QXWXsQ2/l9vHczfx/mwT9L86Y53f+XNGdwnas0RERFoaBafS8pSXwrLPfct+e9b3uPNoMyzQ08PpKnN6UQ6/B0YHYUXbg26F9K51uya5XcChtpbttc3Hsi9h6gNmO49jAwzL9PYvr7ltVqQJaGc+7ZTZtgk0ZN/z1ikm7TAUhl9ogkzP3GTPBxqehbpuWWEW9fIWmwxJ7eHHe50yzxzm+HQzKmHFN7B3U9WrWHv76lZY+hlcN9vsQxpoqO2sZ0za+0hICEJvZ9+jnS2bgqSorJyUuCjevmx/XF4feFkW9GufQkyUBiyJiIhURcGptDxTHwq8Ym7r3nD9bN+ALN897PDffU2a2hnG3Rycdhx2Z811aml53+sZNftGc+CZB7tjiUknXWrmqx50i3OBbZuA2yMqDu7Y6AzX9HCV+w5BlpZvxXfw2XXmww2AoeeYYefjbzdDXV8JsHiX9/Beb3mV9vWN8erJ73+imSe9eU7tgtOl7u1hnh4J/U+CM173Pb9zhbOFzNnv1ny/ECkpc5EUG8WgzNRQN0VERKTZ0W+l0nJsmgPf/h02zjLH7QdDSiczbPHr2+AE9+qj3j2F62f43iOjT9O0tY7yk7rBkf+Eb//mLJa0d5MJLhdNgkU4wamrHP47AvasNcdt+8NV001QO+g0M0fQsuDnR80c1jStELpPeed03+PoBCffZX9IyICCXXDy8zXfK3M0bPrdq8BraLwnUC0vpVZiU8yiSgBLPnXfzoa5r8N+J1QM089PyCSxPguVNYJNewrYnuO7ivbWvUXqHRUREaknBafSMhTugZcOdY6j4uHKn51AdOQlgXsIW3X3nZNZ3oC9RBubZ5ijZ5GZ8hLYvca/3tb5TmAKcMT9Tm9rXKrp0f3JvajMEwPh0u/NMGdp+QKtnpuY4XtcsMuk6d1qvt/Jz8F/hzvH3j2kke79OstquQVSXKoTnIKZg7p6itmTeO4bTnMLNgW4uOmVu2yOfPxn8kvK/c4N6ZzW9A0SERFpARScSsuwea7v8QWTfXtIqxq6esSDMPsV57ht/8D1woGnh2vTHybN3WqGQFZWeaVf754xj+EXmHmrYAIABaf7hgcrDc+NjHX2960sqYqhvN5a9XDyCa1h/N+dY8991/4Mw86t+V6elYM9XjnCWajIayXtFb2vIlTjGzbuLuDHpduxgaJSF/kl5Zy/f1cO7+/7Z9W7bQ0LlYmIiEhACk6leXvnLEjt5OxX6NF+UO2uj6kUuB1+d3Da1Rii3D2nSyZXXSdvJ7x2rG9ZZIDgw3sO4abZDW+bhD/vXvaz3jVDeCOq+RFQuUc1EMuCv66FDbOg3zG+5yKiTfrne3BKDUOE83b6l3UeY1YT9inbny0djwhJcLo+K59D//0T5S7fRY6OGNCOg3rXYh9jERERqZGCU2m+SvJhxde+ZcMvhD/f9w86qxMZC+XFcMtyiEkMbhuDKTq+6nOeQGDld/7nIqOrv++q7+vfJmke9m6Gydeb/MT7zAq1Va3SHBljhozH1LL3L6GVf2AKEJ9mUs+WM9XZuTRAOwL8vT3tZZi3qnbtCoLFW/Zy7FPTGd+3DdOWmwD66IHt+ecp5sOvqMgIkmL1Y1RERCRY9FNVmifbhrmVVp69Yb6ZQ+pZ+Ki2bpgLO5ZBcvugNa9RBApOUzLNfNLs9Wbo45a5/nWqGrZ52F3w431OYCst13MHmnnZAGOvq377oKtmwI7FDd9iKCLSLEjWef+a62at9i8r8t9SidiUhrWpjmauNvO7PYHp8C5pPHveiCZtg4iIyL5Ewak0TzOehB8qDcFt1T1w3ZqkZtZuq4tQCzR3tFV3GHY+fHIFvHio7znP6qeV96f0OOgWyFoD898y23S0Cc+ViqWBbNsJTK+b7SyOVZU2fYL3dyEiEmz/BYP85GzxLytyL46U0Rd2LTd5zyJLTSQ5zvyInH77BGwbMpKq+KBHREREgkLBqTRPG2aa9NyPIHeLWemzpYtN9i877C7oNNIEp5Vd9IXpFY1Pr/qenqGXz4yCewL0VEnzVlYCr7mH3J74DGT0btrnR0T57rcbyKKP4ed/+ZZFxjo9p228g9PG6+XfW1DKB7M3YmPTp10yyXFRrNyeB0BCTBStEps2MBYREdkXKTiV5ue3F2DFN5DUHnofHurWNB3vIPOODaZn1DP0suMw2DLPt35GX2f7maqMuxlmPg2JARZ0mfpP2LYQznrb7K0a17RDKiUIlkx2Vnceck7TP9+KdILT8lKY8QSMuQpW/QDdDzHzVVf/6HtN5zEmqPW02zsgbcT9Tf/30yqe/8l/a6aYyAgSYsJjX1UREZGWTsGpND8/3mvSg24JbTuamncAGZ3oOycwvpVv3Rvm1xyYglmRtd2gwMOaf3rYpPemmfTmxc1j+LM4PAsNHX4PREQ0/fO9e07nvwNTHoCcrTD7ZegwxOxF7L1i8C3LzYcuP94L62eYsj5HwaKPGr2pM1dnMbZHax45dTDrsvIrytulxBEXreBURESkKSg4lebl58egJA/6HgNjAgxlbcksy2wnU1bkv29r5bl4dZl/G5MApfm+ZWUl/vWyNyg4DXcz/wddxkCnEbBnHfzyb7MX6bibQ9OeiChwuUw+b4dJ893bxmxdYNI5rzn1PYuSpXZ2ymIS4cIvYO1PjdrU/OIyOqcn0KW1+RIREZGmp+BUmpcp95vUCkEvUDi4YT7sXOZfHuUVnKbUMYCMToA1U80cv7hUszjNf/bzr+dZVEfCU1kxfPs3k79zF6xyD5ftf1LImkREJCz/Eu5JhQEnm7Kln/nW8Sx4tP+1Tpn3hyCRMdD9IPMVRLZtc97Lv7F6Rz7tUmJZvTOfYV2qmZ8tIiIijW4f/Q1fmh2Xy/yC63HE/aFrSyildICeE/zLY7wWSzr64brdc+PvJv3xfjPHNFBgCvDbc3W7rzQt761XVnxrRhhAaIe/e8+TXvyJ/3nbNiMB+h4LRz3klKd59ZxWHrIeJFv2FjFjVRa5RaUs2WpWBu7aSj2mIiIioaSeU2l8CyfBrpUw/o767534q9fepSe/YIYqiiO9m5MPtC1HddoPhI2/wR8vOqv3BrL2ZzM0s6qtaaRqu9dCWtfGnffp2XoFoLwYSgtNPtD+uE0luUP1511lpkc+rYtveUJrJ58UYLGuOrBtm6z8Elwu26d8lnsP0zcuHcPQzmnsKSihtVbkFRERCSkFp9K4Sgrgo0tNfvAZ0Lpn3a4vK4bXT4CNs8yxFQl9jghuG1uC9oOcfOcxdbv2rHfgUfd7+flR33Nxqb49ci8eBjcvrF8b90Wucpj3Jnx+Iww+E055ofGe5f2eJl1i0qi4Rl3htkZRNewLWpJv9uKt/KFIlNdiXoFWkq6Dt2at587JiwOei4yw2K9DMpERlvYwFRERCQMKTiX4craa3rUNs5w9FgEKs+t2n1nPmmGBnsC08/5w6bdBa2aL0udIOOUl6HeMWUCmLhIzqj7317Vm6OW3f4ffn4e9G6Bgt9kCRGq26CMTmAL8+T4c94RZgKoxBJoTXFbUOM+qragaVox+pKtJK+9T7B3U1tDzuyGrgLd+W095pZ5Rj1lrskiMieTvx/oPV+/aKpGEGP0YFBERCRf6qSzBsXkuzH7FDN19fAB0HA5b5vrWKc4JfG0gudvgmzt8yzSctGqWBYNPD/59Pb1uBVlO2a6VZkVYqdnejb7Hj/WGv28O/nNcLrOoVbgpya1dPb/gNHBQu3RrDqv2lJO+MZuoSIvoyAjemLmOt2ZtICm26h9n4/u25dwxXWvbahEREQkRBacSHJ9eY/ZUnPemOa4cmAIU1/IXVYDti5x8amfzS/4htzesjVK1k56DT6+q+nzBLif/yhHwt80Qm9T47WruVlcKGD2LFAVT/i5nWDZAQobv+wql3etqVy93m++xJzj1mrO6bFsORz/5izn4bYZP9U5p8cy449B6NlJERETChYJTaZjcbWbxktytgc+f/Z7ZFuK5cfDB+XB3NuxeU/3c0+yNZmgwwKF3wgHX1zx3TRpm6Nn+wekdXr1+VqV5izlboE2fxm9Xc7ZlHqz7pXGfUVbsG5ge/S9T9v2d0GMCjLykcZ9fk9r+u608TNyyzLZJXjZkFQBwfv8Yxo8aTGm5TZnLRVm5Ta+2+qBERESkJVBwKjWKKc6Cp4bB6a9Dh8GmcNcqeHqEU8mKhC5jIWsVJLaFHe4FSDoM8V158/MbYO4bcOkP0HmUKdsy3yzoExFp5uhN8vqFetSlCkybSqcRsHmOcxzrtT3Ncf+BJ4c4x9rztGYvjDfp4DPNfNPGkOM1RHjsdTDmSrMI05grw+PfTeXhuae+bObgJmbAnnVO+ZCz/a9t1R2An1fsZOHmvXw423xYMrxtJIft166RGiwiIiKhpH1OpUY9V79uejufPwheOhz+OwJ+vNe3kl0OQ86C21bBxV+Zsm4HQVI780vyoXeasrlvmHTrfJPOeR1eOATua2WeMalST09spblo0njaDTDp4ffArSt9t/1J7wZ9jnKO83c2Zcuan9ztTv6IB+HaP0ygltjWGRUQDEs/d/Ke7ZUiIsMjMAX/dqRmmjm3h93tWx5Z9RYuf/t4IY9+u5x1WQUkxUaRHFPP7ahEREQk7Ck4lerZNq2z/nCON/1hekdL8v3rDj7LpPFpcM9euOgLZ0GdygueZK836bIvnLJPAsx5bMx9IcVXUnuTlhYFXnzqyIecfP6OpmlTc7Txd/i3e8jzsPPNPp1t+pitlPJ3wH/6Bec5JQXw/V0mf8LTMOLi4Nw3mCr3nHqC0NIC33KvD0JcLpvSchdl5S4AcopKuWBsV37/+2FMvXU8UREKTkVERFoqDeuVwKY/AZ2GQ1pXosoLYMRFMOc15/zuNZDeHa6ZBQ+6h9hFV7NthPcQPoCdy01aWuiU5bkDnon3me1Khp3fsO9B6sbzQYLtCnzee4uaL24O/XzGcORywcsTneNh5zn5qOq3RKmzvZtM2vMwGB6m/1Yq95x6gtUqhoXbts2Ef09jvXt+6eDMVPKKy0iJi6ZtSg3b0oiIiEizp+BUHOumw8rvoN9x8IN72F2k+5fLnof5Bqd71pqetug4uHmx7zDGQFI6+h6v/A6ePwQioqDnobD+V3NPMHP0ktsH5VuSOmjlXlgnvVvg89GV9ue0bd+hv+I73PnMt6DL/s6x94c3ZSUQVfVQ1hotnAQfXWryI8Owx9Sjcs+pJ1htPyhg9dJym/VZBRzQszU7covJLy6jd9sk9u/ROmB9ERERaVkUnIrjjRPBVQYznnTKyotN2mGIf/0h7mG8qZnmqzqjr4Rv/27ynUbC5tnOvNOIaN85Z0la7CQkBp1mPhToNi7wee+eU4Dy0oYFWC3RzqUmHXgq7He87znvntOSPIiqtEJtbZUWOoEpVDtfM+Qq//3w/B3qMR6um+O7qBpQ7rIBOLhPG646pJoVvUVERKRF0oQ+cbjKqj6X1gVO+C9cPxeS3b2gE++tun5lkVFmaHBqZ//9Sl2l5ryHeuNCw7Kg+0FV//lHVNpOxvPBxb7EtmH+u7Di28DnP7/JpOP+4n/Ou+e08pzLutj6p3+bwlX3g538aa/4johI7+pXvdRlhpRrXqmIiMi+ST2nYmyZ53t88TfQdSzc417IyLJg+AUmf81MKMqu+zOOd/fIVh4CPOJiM0xRmpfy0lC3oGm5yhn/00nO8T17fc+XFJih6X2PcVY+9ubdc7p9cc2jDariWUwM4Iw3ofcR9btPU+h1ONy6ymwnVXlxs8hov+pl5SbQVnAqIiKyb1Jw2tKUFkJ5if/quDXND1w/06Tnf2p6NzN6meOJ97Nsw3Z81heNTzNf9ZXsNWz3L8vMMN6OQ+GXf8Ml39X/vtK0yvaxntMdS3yPK/+b+t69XdKQswL/W/PueV7wHvQ5svrnrf8VXj0aDr8Xxt3klE990Mn3P6FWTQ+ppDa1rlrm6TmN1KAeERGRfZF+A2hJFn8CD7aHh7v4ln99u9mbtCgn8HXFufDt30y+x3gnMAU48Aa2dZgY8LKgSOlgelRGXAQ3LTTH0jyUl4S6BU1rzTTf48rBed4OwIL9qggYXeVe1xbV/LxXjzbpD157guZnOStfX/RlzfdoImXlLj6cvZGZq7PqfnGiE7yq51RERGTfpuC0pSgrhg8vco7vSTX7Lbpc8NtzsHs1PNwZNsyCvZt9r13m9UtuU833TMgwgbA0X/m7Qt2CprP2F/juH5RGJULH4aascoBZkm+2X6rq35D3nO7CbCe/6geY97Zv3YLdge+xbYFJz3y76oWrmlhxWTlP/biS2yb9ydkvzqrbxXdshBsXVBxOXW62k1LPqYiIyL5Jw3pbApcLHmjrX/7DvXDUP33LXnEPJfTMl3O5zPBBgKP/1XhtrOyvq5vuWdI4Jl8L19YxGGmO8rPg9eMAcEXEmj1Ft8z1DU5t23wAlNG36vvYXj2nhe7gs6wE3jrV5DsMgfYDTX7ydU7diGjnGZ7h910PaMA31DB78kuwLEhLMCvxvjx9LU9NWVVxvs8/vsa2bVw2jOneivtO9J9/m54QQ+ukWIhL8Sn/c6P5f2lsT20dIyIisi9ScNoSLPvcyWeOhk2/m/z66fD8QSYfl+a7iJFnn8UF78Dc101P5pgrm6rF0lxdNxuWTIYp9zvbprRktg0bfq04LI1OIdazd6d3cLpnrRluu/81Vd/Le0Ekz2q9JXlOWfYGJzhd7jWawVVqFp+a/zb87P4AKaGe29DUIK+4jC//3MLJwzKJifLvvVy5PZeJj/8MwDuXj+GAnhls3O278rBt21x+UA9e+mUtv67O4vD//Ox3n8SYSObeNZHYKDMPd+ryHazYlsucDXsYkplKp7R4v2tERESk5VNwGmpz34BdK2HifXUbUusqh60LoMNQZ1huRh+49Dv48wP45Arf+ud/DC8e6hwX7jbbOmS5ezwumNygb0P2ERm9od3AULeiceXthJJciE2BP9939ucFNnQ5mf6e4LTUHZwu/QLeP9fkq+vRHHERFO2FXcth4Ydm6L1niDBAsXtOuM8qyBZgw64VTq9pI3r913U8+u1yIiyL00d29ju/eqcTTM9et4cDemZQWFJO19YJ/PPkQdw26U8O6p3BX4/qx6XjujNzTZbfTjczVu3ivT82srewlLbJkdi2zbVvz6WgxPQsXzDWf4sZERER2TcoOA21z6436egrIM3/l8EqPTfOrB56zoemJ2vkpXDcf8y5IWdCz0PhMa+FjRIyfK8vcAenxXmmV7V9Cw84JHisFjwfsGA3/Luv7xBcj7uz2fHTT/SPcvcUrvwO8rbBIq9tkNrsV/W9o2LgkNucvVDBDA/2KM6FjX/Ay4eb48xR0O9Y+OEeePYAOPivtf42isvKeXn6WvKLA+9d3LV1Ip3TE/h28baKz8RmrdnN0q0mQJ65OosurRL8rrvqLae9U5fvYFiXND7/cyu92iRxQK8MZtzhfADWOimW4wZ39LuHy7Z574+N5BSW0TYZ9haWUlBSzh1H9+OCsV2Jj470u0ZERET2DQpOG1PuNhNA9jzU/1zeTt9fTPdurF1wmrfDLKbi2dZi8cdmeGHmKN963ts3HPlPs+H9OR+YYYrvngkFu8wiLH+82PJ7wiS4WnJwumVu4MAUnJEN0e6eU8/WMd69n5G1+C81ooo6RXth5zLneNAZvnMyC9wLUF1T8zzfZ6asqpgHWnnl2zKXb1dmTFQEsVER5BY5gezH8zbz8bxKC6e5jeuVwe78EuZtyOb8l80Ugsz02g/DTYkzc2hzi0wP8da9pge6c3oCCTH6kSQiIrIv028CjcXTAwNmi5Q0r+1dPPsXenv1aGeRoqrsXAHPVApCF7xr0qgY//o3/mkWafEEx32ONEOBAV4/3qlX1dYXIoE01YrOTaG0CH56BAadBu0GOIsTVdbnqKrvsWuFSY9/snbP/PP9wOXFOWYosYerDGKTneOcrRCfDm2r6Z112+CeB7r2n8dgVXpfO3KLWOBeeCgjKYZhXdIrykc/+CMPnDSQHhmJAe8bEWExrEsatg1Lt+ZQ5rKJiYxgQMeUgPUDSYk3P3b+9vFCUuOjyXEHxe1TY2t9DxEREWmZFJw2hh3L4H9jnOP578D4O0x+w2/+galHaZHTK1NZYbZ/YOotPt2/LL2r+fKWHGAfUe99TUVq0pJ6Tr/+q1kQbPp/4G+BewoZczUc7rXXaNtKq8+W5JnAdMRFtXtmcaX9hlv3MnO/Zzxp8h7ZG2DwmRWHK1evpH1kFMlUr6CkjOzCUvq1T/YLTAHaJscxsb///zNtk+NY9/CxtfseoCKorau+7VOY2L8dOYWm5zQlLooj+rdjvw61D3BFRESkZVJw2hh+vNfJxyTB9kWwaTYkZsAvj/nWHXEx7FgKG2dB/k6zX+l3/4DTXoEYrzlfzx7oe90N8+GpoSY/8DToMaF2bUtqa4YLLvzAKXO5avudibSs4HTtT05++yInP/E+6HO0GeLbpp9vb3FyOzjyIZ+Fkmjbv95NyCqJomLjlCxnS5bVORY9E1vDJd/BK0fQu3wVa0vb8Zc3Zvvdw7ZtTh6WyU8rdvDB7E0A7N+jcVb0baik2ChevGBkqJshIiIiYUjBabBtmQerfjD5noea3tCln5uv+HQo3ONbP6WjWYxo4yzTe7NtIWz6Ax7rDX/36snJMb9wcsFk6HYQRETC9XPNPeu6rUTl3tQeh9Ttetm3ZfQOdQuCp89R8NtzJp/l3nv3pGdh6DnVX1eY7Xtci+B0+bZcZq/fjcs+hvOtryrK1+x18VL5Wdwe/Z5P/Zvmd2R43CJaJaRxo7uso5XF4s17SU1whvHnFpWyaU8hPyzdUVH20MmDGBOmwamIiIhIVRScBkNpkdkSpk1feMM9f3P83+HgW+E+r18QvQPTYx6Dr241PauDz4Qvb4HZrzjnPfsf2rbZ3xDMirw9xjt1WvesX3v7HQs/PwrxreDmRRATeH6ZSEApHWHwWbDWf//KZscTmAJsnW/SjsNqvq7yAmSxSTVe8vdPFjJn/R7gXBZFduLh+LewygoZGbuJfqf+C95zgtOF455h8Y/pLJy5HoAb3aNwY60y3r1if7q29v03u2J7Lnvdw2Qz0+PpkKp9QkVERKT5UXAaDC8eCjsW+5b1Pcr0bgZy6ssw8FTT47nfCRAZbbaS+f0F/7rPjHYWXEluH5z2dhwGd2e3rIVtpGnFJkNZYahbUXe2bVa43jIPRl7ie271FJMmtq35Pn2OMHsMewLaWtieU8Qxg9qzekc+M0qOwTp8IHx+I1ZpAclJXvMtj3yIQWPPY9WhZlXdzdmF8JRzunJgCtCnXU0zUUVERETCXwuaPBZClQNTgPTuJv3LMv+tZHoeagLDgaeawLSyPkdDVBy4yp3AFCCpXfDarMBUGiI63owYaG42zIRJl8Cv/4U5r5uyS741adYqs+dvbYfJDznbpJEBVsquxLZtduUV0yktnklXj+WL68dBSiengvdCaFEmHxFhERFh0dl7v9FOmqspIiIiLZd6ThvKM0/N49B/mF5Qz/6EKR3g/E/g58dgyv3QdVzgX34PuhWWTIa87dBpBKz42ndIMJiFWUTCQXSC6Tl1uSCiGX3GVZDl5Gc8YVLvFazj02v/wc2YK2H9dBh8Jku35rA+y2zfkhwXxQE9W/uslFtQUk5RqYvWSbEku/f5pPvBMOAUOPg2iPLaRiU6wJDcv28xPbvdNT9cREREWi4Fpw318RUm7X8iHP8UxKcFrjf8QrMa6IE3BT6f3A5udfeSznoucJ0OQxrSUpHg8QRQZYXNa85yoN5e7xEJUXXYa9Oy4My3ADjznm8r9usEePqcYSTFRnHf50vAgnKXGaLbOtGrlzUqFk5/1eT3bvYtrywmEfY73r9cREREpAVRcNpQBbtMetjdVQemAElt4PTXanfPyour3LgA8nZUvQeq7FPyisv4ecXOioCnIdbtymdHbnGdrxu9cy/HA//8bC4FUfXb7zKYBnVK5YxRnWuu6N1z6uH978qqYp64l017CioWH5q5OotNewrJKSrj4gO7cVDvDC55bTbXvTOvov6Irul0SI1jZNdWHNKnTeCbeveWRmkxIxEREdk3KTgNhkGn13/l3ECiKgWhaV0hvVvw7i9BsXpnHpe/MZviUrNP7MF92vDPUwY1+nPf/W0DD361NKj3bJVY87xJb5GuIo4Hpi/ZyFYrtHNP84vLmDx/c83BqW2bVaojY6C8JHCdytssVbKnyMVB/5qKXelzgVaJMUzcrx0H9MrgvSv2J7vABK/pCdGM6dE6wJ0q8e591odQIiIiso9ScFpX5WWA7SxkVLTXLKISTN57JrbupcWLwtCK7bkc8bjZSuW4wR1YsiWHqct21HBVcGQXlhAZYfHtTQc1+F4RlkX3jESf+ZG1sigHJsGXV48wWyiF0MNfL+OVGWtrrpi3w4x0GHCyCQbnveWca9sfdiyBrgdUe4vsYhvbhusm9GJQZioWMKZHa1LjnYXN9q9NMFqZ91BezS0XERGRfZSC07r63xjI2wl/22AWgynaC3GpwX1Gu/5w2RTYvQYGnx7cezeRzdmFFJaU1VwRWLYtl2VbczltRCbdMprH/EWzXyUcNaA9T501jPu+WMJHczc1ybOLSl0kREfSq20Itw+Jdr+nkvzQtcEtNiqCkjIXtm1XHWSXFEDeNpPf7wRI7WyCU8+/3dFXwBc3mT2AAygqLefyN2azYrMZAj2ud0b9gtDqjLzEzIlN6Rjc+4qIiIg0EwpO66Ik32w3UXGcB7ar+rmm9ZU5wnw1wLa9RZzx/ExyikrrdJ0FtEuJIzLC/KKfl1dI0p+/1Pr67IJSszdjHT09dRVDO6fV+TqAk4Z25PSRzrBO7xjFwgpYDlBc6uLJH1dSWFpGZITFFQf1pEvrBGqSX2wC70dOG0xEhEVCTCS5RWUc+PCUerW/JkVFRcTNMvfOLighPibE/3Q9cyRLQ7/XaVy0mSf6v2mrK/7OAnRplcDRA9tj7Vph9gtOyTQnEjOckQ+exZBGXGS2dorz2m/Uy4bdBfyychfdUyI4eVhHBnYK8gdSAMc9Hvx7ioiIiDQjCk5rY+UP8OVfzC+wHmUl8MdLJh/M/UeDaNKcjWzcU8C5Y7oQWYdhm0WlLrLynUVydpXmk5Fa+3lwHVLj6Ns+maMGtic+uuYFZgD+WLebde6tOOpq4aZs7vl8Cfd8vqRe13tERlgs3pLDiC7+C/z8snIXa7OcXkLPYkQJMeb7O2lYJ3blFVPualATqrRt2zbat3d66kZ0DfEiRNHuAD4MglMzLBke/Xa537kIC75r9zS9AHLcPdtJ7SDW3es88lKTWlaVgSmYrWAATuwVzU1nDA1e40VERESkgoLTmuTvgrdPNfkf73XKf/4XzH3DLK7S/6SgPCq3qJTf1+5mc3YhJWUNj3K+X7KdTmnxPHBSwxbpmTZtGuPHj2pwe6pz/JD6D2XMLy7jo7mbKCo1AYT3YjXe69b4ljsHqfHRnDO6C498s5w3Z65jxbbcgM/plBbPkQPaVxz3aJNIdKTZ47NPu2T+dVrjbfUzbdoexo8Po62EPHMky0IfnB41sD1L7zvK5/26bJuP523mf1NXUVqQ43tBRh8TjP5tE8RUWhk7gJenr2XWGrPKb2yk5n+LiIiINBYFp9Up2A2PVrEK79YFZr7pmCshqm4rnVbl+Z/W8PTUVTVXrIMj+odnr24wJcZGccHYbg2+zx1H9+OOo7UYTa14hsW6ykPbDre4AD305+/flbzf3mC/PYucwvaDnLHdsdXP2Z2ybDtz12fz9NRVJMZE0rV1Au0TG759j4iIiIgEpuC0OvPfcfIpnSBns8lHxsDK70w+JniL0mTlF5OeEM2kqw+gbXJszRfUQmKo5yZKyxTh/nvlqt2iVyEx839cvecxk49JhpJciIiu/hq31TvzuOS12ebSqAhevXg0o7u3Ytq0aY3UWBERERGpVeRiWdZRwJNAJPCSbdsPVzpvuc8fAxQAF9m2PTfIbW1aZSXw3f+Z/HWz4dWjnXPeeyTW0PviMWtNFos2763yvMu2eff3jXRtnUDPNjUPNRQJqQh3T2W4BqcuF3z7t4rD4rjWxJbkUlAGazbvZd6GPXy2YAuuKjpCPasxP3DSQM7bv/q9T0VEREQkOGoMTi3LigSeASYCm4A/LMv6zLZt79VnjgZ6u7/GAM+60+brl8ecfKuecNFX8MwoOOgWWPo57FphzsXWHEiWu2yufHMOewurXzU3JiqCQ/q0aUirRZqGpwcyXIPT3K0+h0uyoxgWAYu35XH6f6cDZuGuqj4IOqh3BqeNyOTEoZ0avakiIiIiYtSm53Q0sMq27TUAlmW9B5wIeAenJwJv2LZtA7Msy0qzLKuDbdtb/W/XDORnwU+PVBzuyC/hm1UxuCbOByBq0Kmc+dOhRLuKmLqukPWFa6u9XVZ+CXsLS/nXaYM5amD7KuslREcS5V5gRySseYb1bl0Aw84LbVsCyfKdu53RexSsXkXCqPN5occIoiMjGNuzdcC5qiIiIiISGrUJTjsBG72ON+HfKxqoTiegeQania15OP4vDCldwHOxF7HgwR/9qjzB45wVOYVXfs+ggJq3MEmMieSwfm1JiavdnDeRsOYJTn9/AbqNg/4nhrY9le1ebdLMUbDpDzp36QHn7GJARBQD6rCtkoiIiIg0Hcu2q1990rKs04Ejbdu+zH18PjDatu3rvep8CfzTtu3p7uMfgb/atj2n0r2uAK4AaNeu3Yj33nuvxgbm5eWRlNT0czBf/LOYcq8/m87JERySWf/AMiYSYprpNhShegfiCLd3EFWay7gZpsd0Xdcz2NDlNNru+IVt7Q9zVsMNFdtm/E8nAbC+y2l03TCJtd3OZn23sxp863B7D/sivYPQ0zsIPb2D8KD3EHp6B6FX0zuYMGHCHNu2R9b2frXpOd0EdPY6zgS21KMOtm2/ALwAMHLkSHv8+PE1PtzssVlzvWALwSPDVqjegTjC7h0U5cAMk+3WsS3din+E5c/Tb+Qh0HtiaNu26OOKbNejrocXJtH9yKvp3qHh+8SG3XvYB+kdhJ7eQejpHYQHvYfQ0zsIvWC/g9pMcPwD6G1ZVnfLsmKAs4DPKtX5DLjAMvYH9jbb+aYiUrNIr1EE5aXOAkQleaFpjzfv+aYdh8I9eyEIgamIiIiINK4ae05t2y6zLOs64FvMVjKv2La92LKsq9znnwO+wmwjswqzlczFjddkEQm5CK//OlZ8Ax0Gm7wVBgt6FeeY9JbloW2HiIiIiNRJrfY5tW37K0wA6l32nFfeBq4NbtNEJGxZXqvcZq+HgizPiZA0x0dxLiS2geSqV8YWERERkfATBt0cItLsRFT6r8MznDcsek7zIEaLI4iIiIg0N2Hwm6SItBjhEJwumuQ7J1ZEREREmoUw+E1SRFqMiMia6zSmsmKTtuoR2naIiIiISJ0pOBWR4Imo1TT2hsvbCbOegwfaw58fOuU73YsgDT6jadohIiIiIkGj4FREgsd2Nc1z3j8Pvrkdygrhq1ud8p3LTNpuYNO0Q0RERESCRsGpiASPq6xpnrNxlpPPHOXkty8yaVLbpmmHiIiIiARNE43BE5F9gqs8BA+1oSjHLMY040lTFJsSgnaIiIiISEOo51REgqe6ntMdy6BwT/3vvexLmPumyXcaadLeR0Dedni4M/yzE3QZa8pDvTCTiIiIiNSZglMRCZ5tCwOXlxbC/8bAGyfW/97vnQOfXWfyBbtg0BkQl+b7zNgUaD+4/s8QERERkZBRcCoiwfPLY4HL/3jJpFsXNPwZ5WVQtBfiUiF7ve+50gKIjGn4M0RERESkySk4FZHGl7/LpANOqd/1tu3kczaZ4cExiRCb7FuvJA+iYuv3DBEREREJKQWnItL4SgtNWt/AsazIyT85xKS7VsAxj/rWK85rur1WRURERCSo9FuciDSezXNNr2dJvjnes776+lVZ9qV/Wf4uaNXDtyx3G7TrX79niIiIiEhIKTgVkeCybbAsk39xgkn7n2TSDb/W756BekMD7WVakgtJ7er3DBEREREJKQ3rFZHgWvSRSRdOcso8Paf1VVbsX+YZIpyQ4VueGCBoFREREZGwp+BURILrt+dM+tGlTllpgUmTO9bvnkV7/cuOfMik502CCz6D5A7mOFCPqoiIiIiEPQ3rFZHgSMgw+4+W5MPnN/qe8wSn3gsb1UXl4LTP0ZDc3uQ7DjNp7laTKjgVERERaZYUnIpIcMQkmuB0xxLz5a0gy6SeVXvrqigbouKhrBB6HQ5nvV113crDfEVERESkWVBwKiLBUd02MXk7TFpWCCUFEJNQt3sXZUN8Gtyyreo6f98CK7+HzqPqdm8RERERCQuacyoiwRFZTXBaVuSsuFuwq/b3dLlMb+uOpc6c0qrEJMKAk2p/bxEREREJKwpORaR+2uznexwVU339tC4mzd8Jq340+5TW5Ou/woPtYfMc/z1NRURERKRF0bBeEamfK38Gu9wEjwCRNQSnuPc+XfA+/P68yQ88DU5+HiKr+K/ojxedfGKbBjVXRERERMKbek5FpH6iYiA63jmuKTgdfr5JN/7mlC2a5CyWVFnBbt/j7A11b6OIiIiINBsKTkUkOAItiHTGm05+3M0QEQ2dRvjWcZUFvt/0x32PPVvFiIiIiEiLpGG9IhIcgXpOIyLhmMcgPt2p49nz1MMuD3y/yvVOfKbhbRQRERGRsKXgVESCwwowEMO2YfTlznFZIexa6Vunqp7TyuXt+jesfSIiIiIS1jSsV0SCY78T/Mtsl//x5tm+Za4APac7l8Oc14LWNBEREREJfwpORaRhYpJM2nk0dBzme651z5qvD9Rz+vsLvscdhtaraSIiIiLSfCg4FZGG8QSnlgVDz/U9126A77EV6X995eDUVQ5xab5lSW0b1EQRERERCX8KTkWkYWLdwWlpkQlQq3P43f5lf7zkezzlfvjlMef4+KfgpOca1kYRERERCXsKTkWkYfocZdKYhMA9o948vaze5r7pe7zoI9/jERdCYuv6t09EREREmgWt1isiDTPxPhh5CaRmBl6x11tssn/ZwFN8j7M3BK9tIiIiItJsKDgVkYaJiHQWPqopOI1JdPKdx0DOFigtbLy2iYiIiEizoWG9IhI8ETUM642KM2mP8XDpd5DaGYr2Vl3/3I+qPiciIiIiLYqCUxEJnpp6TqPjTVpaZNL4NCjMrrp+78OD0SoRERERaQYUnIpI8HgWRIpOgEt/8D/v6TktcwencWlQlN0ULRMRERGRMKfgVESCx7OVTOfR0HmU/3nPgkieVXvj06BwT5M0TURERETCmxZEEpHg8cw5jYwJfL51LzjqERhwkjmOS4OSPCgvhcjo6uefioiIiEiLpuBURILHM+c0IrqK8xbsf5VzHJ9m0qK9kJgBb5/eqM0TERERkfClYb0iEjyucpNGVhGcVhafblLPokgbfwt6k0RERESkeVBwKiLBU15q0toGp3FpJq28KFLmKLhudrBaJSIiIiLNgIJTEQke291zatWw36mHZ1hv5e1kuh8MGb2D1SoRERERaQYUnIpI8Ni2ST2r9tbE03NaecVe2xW0JomIiIhI86DgVESCyB2cUsvg1NNzuuwL33LP3FURERER2WcoOBWR4Ktrz+mST2H3Gqc8KjbYLRIRERGRMKfgVESCx65jz2mU136oOVug9xEmP+7moDZLRERERMKfglMRCSLPnNN6XFpaaNIOQyEmMVgNEhEREZFmIirUDRCRFqSi57Qe8nfCyu+C1xYRERERaVbUcyoiQVTHYb3eslYHtSUiIiIi0rwoOBWR4KnrVjLePNvJnPlW8NojIiIiIs2GglMRCR5PUGpF1v3awt0mjUkKXntEREREpNlQcCoiwTP4LBhxERx2V+2v6TLWpAXu4DQqLujNEhEREZHwp+BURIInOg6OfxISWtX+mgsmm7QiOI2puq6IiIiItFgKTkUktKJiITbFGdarnlMRERGRfZKCUxEJvegEDesVERER2ccpOBWR0ItJhLJCk4+KDW1bRERERCQkFJyKSOjFJDh59ZyKiIiI7JMUnIpI6Hm2j7EiIC4tpE0RERERkdBQcCoioVdWZNLoRIiMCm1bRERERCQkFJyKSOhtmWfSktzQtkNEREREQkbBqYiIiIiIiIScglMREREREREJOQWnIiIiIiIiEnIKTkVERERERCTkFJyKSOid+XaoWyAiIiIiIabgVERCLz4t1C0QERERkRBTcCoioVdWHOoWiIiIiEiIKTgVkdDrMCTULRARERGREFNwKiKhl5gR6haIiIiISIhFhboBIiIAnPQcFGSFuhUiIiIiEiIKTkUkPAw9O9QtEBEREZEQ0rBeERERERERCTkFpyIiIiIiIhJyCk5FREREREQk5BScioiIiIiISMgpOBUREREREZGQU3AqIiIiIiIiIafgVEREREREREJOwamIiIiIiIiEnIJTERERERERCTkFpyIiIiIiIhJyCk5FREREREQk5BScioiIiIiISMgpOBUREREREZGQU3AqIiIiIiIiIafgVEREREREREJOwamIiIiIiIiEnIJTERERERERCTkFpyIiIiIiIhJylm3boXmwZe0E1teiagawq5GbI9XTOwg9vYPwoPcQenoHoad3EHp6B+FB7yH09A5Cr6Z30NW27Ta1vVnIgtPasixrtm3bI0Pdjn2Z3kHo6R2EB72H0NM7CD29g9DTOwgPeg+hp3cQesF+BxrWKyIiIiIiIiGn4FRERERERERCrjkEpy+EugGidxAG9A7Cg95D6OkdhJ7eQejpHYQHvYfQ0zsIvaC+g7CfcyoiIiIiIiItX3PoORUREREREZEWrsmDU8uyOluWNdWyrKWWZS22LOtGd3kry7K+tyxrpTtN97rmb5ZlrbIsa7llWUcGuOdnlmUtasrvozkL5juwLCvGsqwXLMtaYVnWMsuyTg3F99TcBPkdnG1Z1kLLsv60LOsby7IyQvE9NUd1fQ+WZbV218+zLOvpSvca4X4PqyzLesqyLCsU31NzE6x3YFlWgmVZX7r/H1psWdbDofqemptg/jvwuqd+LtdBkP8v0s/legrye9DP5nqoxzuYaFnWHPef9RzLsg71upd+LtdDsN5BvX8u27bdpF9AB2C4O58MrAD6A/8C7nCX3wE84s73BxYAsUB3YDUQ6XW/U4B3gEVN/b00169gvgPgXuABdz4CyAj199ccvoL1DoAoYIfnz919/T2h/v6ay1c93kMiMA64Cni60r1+B8YCFvA1cHSov7/m8BWsdwAkABPc+RjgF72Dpn0HXvfTz+UQvgP9XA79e9DP5iZ9B8OAju78QGCz1730czmE76C+P5ebvOfUtu2ttm3PdedzgaVAJ+BE4HV3tdeBk9z5E4H37P9v735C5CjCMA7/PlAUlLgYTDBZdQX1sF7WKKLiISBeBE9ePCUggnoL3oLeEhAhioggCF4EEQxRVDwGRRKCHpKw/kNjNOCqKOhBTTAKvh6qGluYjdPdNd2ZzvtAs7s1PUX1vtvzbfXW9EpnJX0DfAXcDhARlwOPA3t7O4ARKJkB8BDwVO7rb0n+R8hTKJhB5O2yfEVwA/B9X8cx75rmIOm0pEPAH/V+IuJqYIOkI0qvwq/wb3Z2DqUykHRG0nv58z+Bo8BiH8cw70plAK7LbZXMANfl1grm4NrcUosMjkmqvrefApdGxCWuy+2VyqBtXR70PacRsUSabX8IbJb0A6RvCrAp77YV+Lb2tLXcBrAHeAY408d4x6hLBhGxkL/eExFHI2J/RGzuZeAj0iUDSX8BjwEfkwrfMvByPyMflylzWM9WUiaV+uuUTaljBvV+FoD7gYPlRzluBTJwXe6oSwauy+V0ycG1uYwWGTwAHJN0FtflIjpmUO9ngSnr8mCT03x19QCwS9Kv59p1QpsiYgW4QdKbsxjfhaBrBqRlK4vAYUnbgCPAvuIDHbEC58HFpAJ4C7AFWAV2Fx/oyDXIYd0uJrT5VugNFMig6uci4DXgeUlflxrfhaBrBq7L3RU4D1yXCyhwLrg2d9Q0g4i4GXgaeKRqmrCb63IDBTKo2hvV5UEmp/mkPQC8KumN3Pxj/hN8tUTup9y+BlxTe/oi6SrUncCtEXEKOATcFBHvz37041Aog59JV8erX0T2A9tmPPTRKJTBCoCkk3nZyuvAXbMf/Xg0zGE9a/x3qUqVj02hUAaVl4ATkp4rPtARK5SB63IHhTJwXe6oUA4r4NrcVtMMImKR9DO/Q9LJ3Oy63EGhDCqN6vIQd+sN0tKGzyU9W3vobWBn/nwn8Fat/cG8fvx64EbgI0kvStoiaYn0ZvQvJW3v4xjmXcEMBLwDbM/73QN8NuPhj0KpDIDvgOWIuCrvdy/pvQE2hRY5TJSXt/wWEXfkPnf833MsKZVB7msvcAWwq/AwR63geeC63FLBDFyXOyj4euTa3FLTDPJy0XeB3ZIOVzu7LrdXKoP8WPO6rP7vAHU36c/qq8DxvN0HbCStQz6RP15Ze84TpLuTfsGEuzwBS/iugINkAFwHfJD7OghcO/TxzcNWOINHSUVvlfRLycahj29etpY5nAJ+AX4nXZldzu23AZ/kjF4AYujjm4etVAakq+LK50LVz8NDH988bCXPg9rjS7guD5IBrsvnSw6uzT1kADwJnK7texzYlB9zXR4wA1rW5cidmpmZmZmZmQ1m0Lv1mpmZmZmZmYEnp2ZmZmZmZnYe8OTUzMzMzMzMBufJqZmZmZmZmQ3Ok1MzMzMzMzMbnCenZmZmZmZmNjhPTs3MzMzMzGxwnpyamZmZmZnZ4P4B0ZlF6Bo1RIEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# devised trading strategy\n",
    "# herding means market is irrational, there for index price will correct itself\n",
    "\n",
    "df['signal'] = 0\n",
    "CSSD_threshold = np.percentile(df['CSSD'],30)\n",
    "print(CSSD_threshold)\n",
    "\n",
    "for i in range(0,len(df)):\n",
    "    if df['bear'][i]==1 and (df['CSSD'][i]<CSSD_threshold):\n",
    "        df['signal'][i] = 1 # reverse trend and buy\n",
    "    if df['bull'][i]==1 and (df['CSSD'][i]<CSSD_threshold):\n",
    "        df['signal'][i] = -1 # reverse trend and sell\n",
    "\n",
    "df['profit']=df['signal'][:-1]*(df['return_SPY'].shift(-1))\n",
    "df=df[:-1]\n",
    "#pd.set_option('display.max_rows', 100)\n",
    "display(df)\n",
    "#pd.set_option('display.max_rows', 10)\n",
    "\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(df['profit'].cumsum())\n",
    "plt.plot(df['return_SPY'].cumsum())\n",
    "plt.legend(['strategy','index'])\n",
    "plt.grid()\n",
    "\n",
    "print(f'Annualized return: {round(np.power(df[\"profit\"].cumsum()[-1]*100,250/len(df)),2)}%')\n",
    "print(f'Annualized Sharpe ratio: {round(np.power(df[\"profit\"].mean()/df[\"profit\"].std(),250/len(df)),2)}')\n",
    "print(f'Single max drawdown: {round(df[\"profit\"].min()*100,4)}%')\n",
    "print(f'Average number of trades generated annually: {round(np.count_nonzero(df[\"signal\"]!=0)/(len(df)/250))}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>CSSD</td>       <th>  R-squared:         </th>  <td>   0.043</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.043</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   94.73</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 10 May 2021</td> <th>  Prob (F-statistic):</th>  <td>5.75e-41</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>00:25:32</td>     <th>  Log-Likelihood:    </th>  <td>  13734.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  4202</td>      <th>  AIC:               </th> <td>-2.746e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  4199</td>      <th>  BIC:               </th> <td>-2.744e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    0.0112</td> <td>    0.000</td> <td>   61.192</td> <td> 0.000</td> <td>    0.011</td> <td>    0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bull</th>      <td>    0.0037</td> <td>    0.000</td> <td>   10.217</td> <td> 0.000</td> <td>    0.003</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>bear</th>      <td>    0.0042</td> <td>    0.000</td> <td>   11.486</td> <td> 0.000</td> <td>    0.003</td> <td>    0.005</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>2937.534</td> <th>  Durbin-Watson:     </th> <td>   1.262</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>51592.058</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>           <td> 3.156</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>       <td>18.964</td>  <th>  Cond. No.          </th> <td>    3.15</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                   CSSD   R-squared:                       0.043\n",
       "Model:                            OLS   Adj. R-squared:                  0.043\n",
       "Method:                 Least Squares   F-statistic:                     94.73\n",
       "Date:                Mon, 10 May 2021   Prob (F-statistic):           5.75e-41\n",
       "Time:                        00:25:32   Log-Likelihood:                 13734.\n",
       "No. Observations:                4202   AIC:                        -2.746e+04\n",
       "Df Residuals:                    4199   BIC:                        -2.744e+04\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      0.0112      0.000     61.192      0.000       0.011       0.012\n",
       "bull           0.0037      0.000     10.217      0.000       0.003       0.004\n",
       "bear           0.0042      0.000     11.486      0.000       0.003       0.005\n",
       "==============================================================================\n",
       "Omnibus:                     2937.534   Durbin-Watson:                   1.262\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            51592.058\n",
       "Skew:                           3.156   Prob(JB):                         0.00\n",
       "Kurtosis:                      18.964   Cond. No.                         3.15\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CSSD regression\n",
    "\n",
    "m = sm.formula.ols(formula='CSSD~bull+bear',data=df)\n",
    "model = m.fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CSAD regression\n",
    "\n",
    "df['abs_return_SPY'] = abs(df['return_SPY'])\n",
    "df['square_return_SPY'] = np.power(df['return_SPY'],2)\n",
    "\n",
    "m = RollingOLS.from_formula('CSAD~return_SPY+abs_return_SPY+square_return_SPY',data=df,window=60)\n",
    "model = m.fit()\n",
    "\n",
    "#plt.figure(figsize=(16,8))\n",
    "fig, ax = plt.subplots(figsize=(16,8))\n",
    "plt.title('Herding occurs below red line')\n",
    "plt.plot(model.tvalues['square_return_SPY'])\n",
    "plt.legend(['rolling 60 day t-stat for CSAD regression'],loc='upper left')\n",
    "plt.axhline(y=-2,color='red')\n",
    "plt.grid()\n",
    "\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(df['SPY'],color='green')\n",
    "ax2.legend(['index price'],loc='upper right')\n",
    "\n",
    "ax2.xaxis.set_major_locator(mdates.MonthLocator(interval=12))\n",
    "# Minor ticks every month.\n",
    "fmt_month = mdates.MonthLocator(interval=6)\n",
    "ax2.xaxis.set_minor_locator(fmt_month)\n",
    "# Text in the x axis will be displayed in 'YYYY-mm' format.\n",
    "ax2.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}